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Reliability Tools: |
Reliability tools exist by the dozens: what are the tools, why use the tools, when should
I use the tools, and where should I use the tools? Click on the tools below for answers.
The details about these tools will be brief
as books are written about each item.
Think of the presentations below as hors d’oeuvres (a little snack food
or starters)—not the main course.
The most important reliability tool is a Pareto
distribution based on money—specifically based on the cost of unreliability which directs
attention to work on the most important money problem first. No magic bullet exists for reliability issues, don’t waste your time looking for a single
magic tool—none exist!
What: A test method
of increasing loads to quickly produce age-to-failure data with only a few data
points are then scaled to reflect normal loads.
Why: The benefit
of accelerated testing is to save time and money while quantifying the
relationships between stress and performance along with identifying design and
manufacturing deficiencies to get useful data quickly and at low cost.
When: Usually
performed during the development of devices, components, or systems. Also applies to items that have been in
service to obtain a metric needed to show how the item is performing under
heavy loads. Accelerate testing is a
useful method for solving old, nagging, problems within a production process.
Where: Used for
correlating test results with real life conditions.
What: A tool for
measuring the percent of time an item or system is in a state of readiness where
it is operable and can be committed to use when called upon. Availability ceases because of a downing
event that causes the item/system to become unavailable to initiate a mission
when called upon. In the simplest view
the metric is availability = uptime/(uptime +
downtime). For many other definitions
see MIL-HDBK-338,
section 5.
Why: The measure
is important for knowing the commitment of time for performing the mission and
it usually only involves the use of arithmetic.
When: Often the
measurement tool is based on past experiences and the complement of the
measurement tool addresses unavailability to perform the task.
Where: In design of
a system it is a calculated value and in operation of a system it is a
performance index that is often easy to use and provides an index that is
understandable to the average person.
Today there is a great tendency to “Enronize”
availability metrics by using uptime metrics that present data in the best
light (an issue of data integrity) to maximize managerial bonuses by excusing
(deducting) downtime from the calculations to put “lipstick on the pig”. Use the KISS
principle. Think of availability in
terms of the investor’s typical year of 8760 hours. The no-excuse annual metric in hours is
availability = uptime/8760. Suddenly
you’ll find a metric of great interest to investors that can be benchmarked as
a financial issue, and thus motivate the management team to solve real issues
of importance to the business. Please
note, you can have high availability but many failures
and thus low reliability as availability
≠ reliability. Likewise, you can have high availability, but
little output so team the metric with effectiveness
to get the complete story.
What: The concept
is derived from the human life experience involving infant mortality, chance
failures, plus a wearout period of life since data for births and deaths is
accumulated by government agencies. Most
equipment lacks the birth/death recording by government agencies and most
non-human systems can be regenerated to live/die many times before relegation
to the scrap heap.
Why: Failure rates
are different for both people and equipment at different phases of operation
and the medicine to be applied to both humans and equipment need to be
considered for effectively treating the roots of the problem.
When: The concept
is useful during design, operation, and maintenance of equipment and systems to
understand the failure mechanisms
Where: It explains
the human experiences to the ordinary person to relate equipment/system
failures to those experienced in real life so as to coordinate the design,
operation and maintenance of equipment.
For other definitions see MIL-HDBK-338,
section 9.
Block Diagram
Model (same as Reliability Block Diagram Models)-
What: Reliability
block diagram (RBD) models are graphical representations of a calculation
methodology for reliability systems.
Why: The RBD
models allow calculation of system reliability based on knowing/assuming
failure details of the components, starting with the least component and
growing the model to the greatest system to predict performance from the elements.
When: RBDs are used in upfront
designs as a performance parameter and after the system is constructed to
ferret out poor performing blocks that limit the system performance.
Where: Frequently
used as a trade-off tool to search for the lowest long cost of ownership and to
help sell alternative courses of action for moderating the effects of
reliability issues or overcoming the poor performance by alternative designs
where the results can be calculated before building the system as the results
of the calculations provide knowledge about availability, maintenance
interventions required for failures, and the number of spare parts required to
sustain operations. For other
definitions see MIL-HDBK-338,
sections 4 and 6.
What: A measure of
how well the product performance meets objectives. In short, how well are the outputs actually
accomplished against a standard? Capability
is frequently the product of efficiency * utilization.
Why: Capability is
a component of the effectiveness equation and
usually under the control of production.
When: Data for this
metric is frequently produced by the accounting department each month as a
segment of the financial reports for the purpose of handling variances against
the standards.
Where: Frequently in
the effectiveness measure it is a weak point (as a measure of how well the
production process des the job for which it was purchased) requiring
substantial improvement that cannot be solved by the usual reliability and
maintainability (
What: Configuration
control is involved with the management of change by providing traceability of failures
back into the design standard. If the
design details are not specified, the design will not contain the requirements
and thus implementation of the project will be hit or miss for achieving the
desired end results, beginning with the conceptual design and resulting in the
operating facility.
Why: With active
configuration control you know where items are used and contained, where and
why they were installed, where signal originate, what items are used where and
in what environments, what drawing revisions have occurred and you know if the
product conforms to the drawings and specifications, what alternate
materials/components have been used, and what test reports/certifications are
available as original documents for review.
When: Configuration
control begins after the first design review to build an unbroken chain of
traceability to aid in avoiding surprises in the field which would destroy the
designed-in criteria for availability, reliability, maintainability, and cost
effectiveness established as a portion of the original design criteria.
Where: Frequently
these documentation details are assembled into a dossier with third party
witnessing for use in validating conformance to the design requirements and
provided to the owner of the equipment as witness documents.
What: Tell your vendors what you want, and want
what you say. Provide explanations of
the objectives in written contracts in terms the vendors will understand.
Why: If you can’t
clearly spell out the requirements for availability, reliability, and
maintainability the contractors cannot make these issues features of the
design. Thus, it is important to be
specific in the features the design must manifest. Explanations such as: “You know what I want
and what I need, just do it quickly” are
self-defeating expressions of vague generalities that lead to inferior designs
and constant arguments. Be specific
about requirements for building reliability
block diagrams, using quality function deployment,
performing failure mode and effects analysis, conducting fault tree analysis, and finally, conducting design reviews for reliability.
When: Write the
specifications before procurement begins.
Plan to spend time with your own purchasing department to explain the details
and sell the team on the financial advantages for including reliability
requirements into the specifications.
Likewise, spend time selling your vendors on the requirements and why
they are stated.
Where: These are up
front decisions to avoid replication of previous problems that were built into
previous designs and never corrected.
What: The cost of unreliability
is a big-picture view of system failure costs, described in annual terms, for a
manufacturing plant as if the key elements were reduced to a series block
diagram for simplicity. It looks at the
production system and reduces the complexity to a simple series system where
failure of a single item/equipment/system/processing-complex causes the loss of
productive output along with the total cost incurred for the failure. If the system IS sold out, then the cost of unreliability
must include all appropriate business costs such as lost gross margin plus
repair costs, scrap incurred, etc. If
the system is NOT sold out, and make-up time is available in the financial
year, then lost gross margin for the failure cannot be counted. The cost of unreliability is a
management concern connected to management’s two favorite metrics: time and
money.
Why: In private
enterprise, failures must be concerned from a financial viewpoint and not a
“gear-head” approach of simply counting the number of failures; you must also
speak the language of the enterprise, which describes events by monetary measures
over a period of time. The annual cost
for failures is usually not stated in a clear-cut manner nor are failure costs
summarized by a system/sub-system to identify the weak links in a monetary
fashion so that appropriate action is taken to reduce the annual cost of unreliability
by building a clear Pareto distribution to
attack the vital (high cost) areas with an action plan to reduce failures
(unreliability) and to reduce the cost of unreliability.
When: For new a new
plant, this can be a design criteria to limit costs of
unreliability for competitive reasons in the marketplace. You must make the hidden costs of failures
obvious as a portion of the strategic plan.
For an existing plant, this can be an exercise in defining the cost of
unreliability and building a long-term plan to reduce the cost of failures as a
portion of the tactical plan.
Where: This activity
is best performed with high-level involvement of the management team to provide
fundamental understanding of the size of the icebergs about to rip out the
underbelly of the plant and to involve the organization in a plan to reduce the
costs so that profits are pushed upward because of the improvements. If the cost of unreliability cannot be
reduced, then the costs become extra weight for the saddlebags in the race for
survival.
What: The critical
items list is a top-level summary of problems/cost used for discussions with
management about key reliability issues.
The summary list converts technical details to a summary of costs and
time while placing the issues into a Pareto distribution explained in terms of
money and the vital few problems to be solved for competitive reasons.
Why: The purpose
of the critical items list is to focus management’s attention on items that
need to be resolved during the design phase as a corrective action loop for
influencing the lifetime costs.
When: The list
starts with the first design review as issues are disclosed in design reviews
for reliability.
Where: The critical
items list is presented to top-level management as issues to be accepted or
resolved before paper plans become steel and concrete.
What: Data is the informational
energy that runs the reliability improvement machine. Data is acquired at great cost. Data needs to be retained and used to prevent
future failure events. Proper use of data
provides an understanding of failure mechanisms and prevents reoccurrence of
bad events that cause safety or high-cost failures to occur. Reliability data requires definition of a
failure. Failures can be catastrophic
failures or slow degradation—you decide by defining the failures. The units of the measure for the data must be
in units of the degradation—sometimes it is hours, some times it is miles, and
so forth—in short, whatever motivates the failure. Reliability always ceases with a failure or a
removal from service in some aged condition that then generates a category of
data called a suspension or censored data.
Data is information in the form of facts, figures, or engineering
databases that is obtained from engineering tests, experiments, or actual
operating conditions. Reliability data
is often incomplete as the exact times to failure are rarely known or recorded
with much precision so that only partial information is available for analysis. Reliability data comes in two forms: 1)
age-to-failure data, and 2) censored/suspended data such as occurs when
unfailed items are removed from service or when they fail due to a different
failure mode than we are studying—this is useful information and part of the
data set. Some data is better than no data
for resolving reliability issues.
Why: Data is the
information that, when used in an informed manner, helps prevent repetition of
bad history and allows an enlightened approach to rationally solving a
reliability issue using facts and figures.
Intelligent use of data for reliability issues provides the objective
evidence needed for helping to solve the root cause of failures.
When: Databases of
reliability information of past experience is very helpful for predicting
future failure events. The data is
helpful if failure rates, or the reciprocal of failures rates is described in
mean times to failure which reduces the information to an average failure rate
or average time to failure. The
reliability data is particularly valuable if retained for components as a
Weibull database with shape factor beta and scale factor eta.
Where: The data is
useful for understanding failure modes, for predicting future failures for a
population of equipment during the design stage, and for predicting future
failures with subsequent increases in the aging of equipment. The role of the reliability engineer is to
acquire the failure data and convert the data into useful information for both
current and future use.
What: Most business
decisions have considerable uncertainty, which implies at least two outcomes if
you choose a course of action. Making
decisions in the face of uncertainty requires the costs for taking action and
the probability along with the cost for not taking action and the probability
of the occurrence. In most cases the
probabilities are not well known (maybe to one significant digit) and the costs
are not well known (maybe to $10000).
The quantitative assessment is called risk assessment. The issue is to take these not-well
identified issues and devise a strategy that can minimize exposure to risk for
the business. Decision trees are graphical representation of a methodology to
reach the expected values for the decision so as to take or not-take action.
Why: Most business
decisions have no exact answers, i.e., no black and white answers but rather
shades of gray. The use of the tool is
to help decide which course of action may be to the advantage of the business
given the best estimates that can be made.
When: Decisive
details will only be known into the future and decisions have to be made today,
so use of decision trees are tools to help wisely span from today into the
future with the wisest decisions that can be made from sketchy data.
Where: If you have
absolute data, use it. Most decisions
must be made with indecisive information that requires decisions about the odds
for a given event, usually based on estimates—the wiser the estimate the better
the decision, taking into account the probabilities of the outcomes and the
money involved in the decision. Use this
tool when few details are available and you must be the pioneer to cut through
the forest to reach the promised land of opportunity and profitable ventures.
What: The
International Electrical Congress (IEC)
defines dependability as “Dependability describes the availability
performance and its influencing factors: reliability
performance, maintainability performance and maintenance support performance.” MIL-HDBK-338
defines dependability differently, as a measure of the degree to which an item
is operable and capable of performing its required function at any (random)
time during a specified mission profile, given that the item is available at
mission start. (Item state during a
mission includes the combined effects of the mission-related system R&M
parameters but excludes non-mission time; see availability.) Dependability is related to reliability with
the intention that dependability would be a more general concept than the
measurable issues of reliability, maintainability, and maintenance.
Why: The key
dependability issue is to make equipment and processes work as advertised,
which is, without failure. Dependability
aims at facilitating cooperation by all parties concerned (supplier,
organization, and customer by fostering an understanding of the dependability
needs and value to achieve the overall dependability objectives), so it
involves harmonizing conflicting issues.
Dependability has a better viewpoint from the end user of the equipment
or system than from the designer’s viewpoint or the maintainer’s
viewpoint. From a system-effectiveness
viewpoint, reliability and maintainability provide system availability and
dependability.
When: You cannot
repair yourself to happiness with a failure prone system as the failure-prone
system will be viewed as lacking dependability to function as required when you
need it. Thus, dependability is viewed
over the longer term and not in convenient snapshots, and dependability also
involves lifecycle cost issues.
Where: Reliability
contributes directly to uptime by avoiding failures whereas maintainability
contributes directly to reducing downtime by faster repairs. Thus, reliability and maintainability jointly
provide impact on dependability of the system.
Dependable systems must be ready to function, in an operable state, to
produce the desired output, upon demand by the end user, at the specified
quantity and quality of output.
Design Reviews For Reliability-
What: Specific
questions to ask of design engineers during a review specifically for
reliability using failure data from operations and maintenance are: 1) Show the
calculated availability for the system based on a RAM model, 2) Show the calculated number
of failures during the specified mission time between turnarounds based on a
reliability and maintainability (
Why: Design
reviews should demonstrate by calculation or through the use of models and
reliability tools that the system is capable of achieving the design objects
rather than making a giant leap of faith that all will be well and good.
Problems found in the design review for reliability are corrected less expensively
on paper than when corrections must be made in the field with hardware.
When: Design
reviews for reliability should be a part of the design process starting with
conceptual designs and ending when the drawings are revised for the as-built
system.
Where: This is a logical
extension of the design process to show, rather than tell, how the system will
function. This is performed as a portion
of the up-front design by the numbers process.
What: The potential
or actual probability of a system to perform a mission for a given level of
performance under specified operating conditions is defined as the product of reliability*availability*maintainability*capability
(dependability is often defined as reliability*maintainability) and all values
of the product are between 0 and 1. Many
variants of the effectiveness equation exist, e.g., OEE, and others. See a parallel comparison with system
effectiveness based productive output results of process
reliability calculations.
Why: The
effectiveness equation defines the ability of a product, operating under
specified conditions, to meet operational demands when called upon. This is a practical measure of how well the
system is performing—not how well we want it to perform, but it is a practical
measure of how the system is doing.
Since all the elements are measured between 0 to 1, the elements of the
equation quickly draw the eye to where opportunities exist for making
improvements.
When: The
effectiveness equation is useful for trade-off boxes for various alternatives
when plotted on an X-Y scale for effectiveness vs net
present value (NPV) for showing improvement alternatives. For the elements::
reliability defines the probability
of a failure-free interval (or the complement unreliability which describes the
probability of failure);
availability defines the probability
of the system being up and alive to handle the demand (or the complement,
unavailability which describes the probability of the system being down);
maintainability defines the
probability of making repairs within the allowed repair standard;
capability defines the probability
of production achieving the desired production results (a measure of how well
the product performs compared to the standard).
Frequently it is described as the product of efficiency * utilization
where
efficiency is an output/input
relationship such as (output achieved)/(the standard
required) and
utilization is how time is used such as
(direct labor)/(direct labor + labor lost)
[In the old days, if this index decreased
to as low as 80% we went berserk—today,
you can’t get this high because of wasted
time when noses are not to the grindstone!!!].
Where: It is used to
describe the performance of both new systems and old systems. Consider this example for effectiveness: If we are comparing a heavy-duty truck versus
a sports car for transportation, the truck may be more effective for heavy
loads whereas the sports car may be more effective for acceleration and high
speeds—neither are defined by the effectiveness equation until the mission is
defined. The effectiveness index is
converted into output quantities by use of the process
reliability technique for quantifying the productive plant and the
non-productive hidden plant based on a pragmatic definition of nameplate
capacity for the plant.
What: Electronic
components are everywhere, and they are getting smaller and more complex by the
year! They are becoming a larger part of
modern society every day. As a class,
they are particularly susceptible to increased failures from temperature,
vibration, and shock loading which destroys reliability.
Why: Most
electronic devices are small and delicate.
Inherent failure rates are often built into the device by the
manufacturing process (similar to building in human genetic defects), and you
cannot find the inherent defects until the components are stressed. The best remedy for electronic devices to
achieve high reliability is to start with a high quality, durable devices built
on a failure-free process, load the devices only to moderate loads, and to
carefully control the environment to suit the needs of the electronic
component.
When: Burn-in
tests, of different degrees of severity, following assembly of the system is
imposed to weed out the inherent defects by adding stresses due to temperature,
vibration, and shock loadings to cause the weak units to fail. Other accelerated tests for electronic
devices include ESS, HALT, and HASS.
Where: The usual
failure rate distribution for electronic systems is considered to be the exponential distribution, although some
electronic devices such as SCRs often display a decreasing failure rate described
as infant mortality failure modes by Weibull
analysis, and some electronic devices have an increasing failure rate
described as a wearout failure mode for devices such as electrolytic capacitors
and EPROMS. Many electronic failure rates and electronic
models are available in MIL-HDBK-217
and it’s successor PRISM.
Environmental Stress Screening (
What: A series of
screens are conducted under environmental stresses to disclose weak parts and
workmanship defects that require corrections, and this requires and understanding
of burn-in testing and
Why: The extremes
of operating conditions such as high power levels, high temperatures, high
vibration levels, etc. produce failures not anticipated from testing at nominal
conditions. Generally,
When: When
acquiring data, the tests are done upfront of production. When controlling early failures that would be
discovered by the end user, these test are done as a
portion of the production process to eliminate weak units to control warranty
costs and improve customer satisfactions
Where: Some tests
are conducted in the laboratory for quick results and then the data is used to
control product testing/release for the purpose of limiting costs and
preventing the loss of customers from unsatisfactory performance in the field.
What: Events/incidents
are single events or occurrences, especially one that is particularly
significant, that result in a failure from an
non-aging mechanism for reliability purposes.
Usually the event/incident results in a serious consequence of the loss
of functional life of a component or system.
The death of the device must be recorded as censored (suspended) data.
Why: For
reliability purposes, failure of the component, device, subassembly, or system
has been a success up to the point in life where a failure from a non-aging
event took place. This means the event-age
was a success (up to the point it was killed by an event/incident) and
inclusion of the data is required as censored/suspended data—this is important
data.
When: Include the
suspended/censored data into every analysis.
Young suspensions/censored data have little impact on the results of an
analysis but old suspensions have major effect on the analysis.
Where: The data is
used for MTBF/MTTF analysis and particularly for Weibull analysis.
What: The
probability of survival and of failure of components or equipment is under the
condition of chance failure ,which means a constant
instantaneous failure rate where the die-off rate is the same for any surviving
(unfailed) population. An old part is as
good as a new part. For any survivors in
this memory-less system that have survived to time t, a certain percent of the
survivors will die in a specified interval of time such as 2*t. The reliability of the system is often
described by the exponential distribution because many times a system is made
up of mixed failure modes that in the aggregate will function like a constant
failure rate system. The reliability of
exponential distributions are described mathematically as R(t)
= e^(-lt) = e^(-t/Q) where t is the mission time, l is
the failure rate, and Q is the mean time, given that l=1/Q. The exponential distribution is frequently
used as a first approximation to describe reliability based on a simple failure
rate or a simple mean time to failure—particularly if the system or component
has multiple failure modes.
Why: The constant
hazard rate, l, is usually a result of combining many failure rates into a
single number.
When: The
exponential distribution is frequently used for reliability calculations as a
first cut based on it’s simplicity to generate the first estimate of
reliability when more details about failure modes are not described.
Where: In electronic
systems (which can have many different types of failure modes, especially since
any electrical/electronic system is an amalgam of many different components)
the simple assumption is that the electrical/electronic package will have a
constant failure rate system defined by the exponential distribution. When in doubt about the failure mechanisms,
it is common to assume use of the exponential distribution with its constant
failure rate for simplicity.
What: Failure is
the loss of function when you needed the function to occur. Failures for reliability purposes must be
precisely defined so they are recorded correctly. Much life data is incomplete because failures
are mixed up with censored/suspended data where aged items may not have failed
or they represent removals from service before failure, or they have not yet
failed for the mode of failure under study—in short, these censored/suspended
items represent successes and are a portion of data set for study.
Why: We study
failed items for the same reason we do autopsies on humans—we want the data and
we want it categorized correctly for making important decisions. Failures require: 1) a time origin that must
be unambiguously defined, 2) a scale for measuring the passage of time/starts/stops/etc.
which motivates failure, and 3) the meaning of failure must be entirely clear
for recording the event.
When: Failure data
must be recorded as it occurs to prevent loss of information.
Failure causes involve design issues, manufacturing issues, assembly
issues, installation issues, or use issues that consume life and motivate
failures by misuse, inherent weakness, or consumption of life by means of a
wearout failure issue.
Failure modes describe the effects under which a failure is observed
including early failures where failure rates decline with usage (infant
mortality), where failure rates are constant with usage (chance failures
describe the usual mid life constant failure rate mortality), and increasing
failure rates with usage (wearout failure rates).
Failure mechanisms describe the physical, chemical, metallurgical, or
other processes which motivate the failures.
Failure criteria are the basis for registering the gravity of a failure
and sometimes temporary changes in the failure state, including duration of the
failure, have an important bearing on how a failure is recorded with the two
largest classifications of failure as complete failure (can’t complete
the intended function) or partial failure (not a complete failure but
deficient in providing all features of the intended function to a level that is
noticeable and undesirable).
Failure onset can be gradual (monitoring is intended to anticipate
detection of pending failure), intermittent (failure occurs in some magnitude
but recovers to complete the intended function), and sudden failure (surprise
events that cannot be anticipated with prior examination or monitoring).
Failure consequences can also be categorized such as critical failures
(significant damage occurs and/or injury to people occurs), major failures
(less severe than a critical failure but of such a magnitude as to
substantially reduce the required function), minor failures (reduces the
performance of the asset but oncly caused minor
consequences for the entire system), and benign failures (failures known and
observed by an expert but not detected by a novice).
Where: The CMMS
system is frequently where most data resides but usually in crude fashion. The failure data is often transferred into
the FRACAS system for converting the symptoms of
the failure into the root causes of failure.
The failure data must be converted into action items for making
management decisions about future failures and the corrective action needed.
What: Failure
forecasting is a projection of failures into the future based on assumed or
documented failure details. It is also
known as risk analysis of future failures.
For a constant failure mode system this is very straightforward. However, for complicated failure modes where
the failure rate increases with time (wearout failure modes) or where failure
rates decrease with time (infant-mortality failure modes), this becomes a more
complicated analysis as described by the Abernethy Risk which is described in The New Weibull Handbook and
implemented in the software package WinSMITH
Weibull for predicting future failures.
Likewise, reliability block diagrams are useful for predicting future
failures when the authentic failure details are supplied to the
Please note manufacturers follow two general strategies for their equipment:
1)
build the equipment to avoid failures even though this increases the original
capital costs, or
2)
build equipment and sell the original equipment at a low cost (or even a
break-even cost),
expecting to make profits with the sale of
replacement parts.
Thus for end users of the procured equipment, it is important to know the
forecasted failures in the face of supplier protests that “our equipment never
fails”—in that case, ask to see the sale of spare parts for similar equipment
and an estimate of the number of units working to get a crude estimate of the
strategy employed by the equipment supplier.
A failure is an event that renders equipment as non-useful for the intended or
specified purpose during a designated time interval. The failure can be sudden, partial, or
one-shot, intermittent, gradual, complete, or catastrophic. The degree of failure can be degradation or
gradual, sudden, or one-shot, from weakness, from imperfections, from misuse,
and so forth.
A failure mechanism includes a variety of physical processes that results in
failure from chemical, electrical, thermal, or other insults.
Why: Future
failures cost money and frequently increase the risk for safety or
environmental problems. For
manufacturers, the forecasted failures predict impending high costs for
warranty expenses which can make/break a company. With good failure forecasts, you can
anticipate expected failures now (after x-usage), future failures when failed
units are not replaced, and future failures when failed units are replaced
either with the same failure modes or with differently designed components with
different failure details.
When: This analysis
is wisely performed during the design of the equipment, however many surprises
arise from different failure modes built into the assembled product or incurred
by unanticipated usage in operations.
Where: Generally
this analysis is made during the up-front design effort—with much disbelief the
products could be “this bad”. Follow-up
analysis occurs when unexpected failure modes arise during operation of the
equipment, which causes loss of service of the equipment and high costs for the
end users.
What: Failure
rates, in the simplest form, are S(time in use)/S(number of failures) or the reciprocal of
mean times to/between failure. For more
sophisticated failure data bases such as Weibull databases the failure rates
can be disclosed without giving away proprietary data such as the shape factor,
beta, which tell the failure mode for the equipment.
Why: Simple
failure rates are a precursor of maintenance events and production
interruptions that will occur into the future, which drive up costs and cause
chaos.
When: Failure rates
derive from the history of operation or from well-known data sources such as
OREADA, IEEE 500, IEEE 493, EPRI, and other sources listed in reading lists for reliability
including Weibull databases.
Where: The failure rates
are used as an awareness criteria for the average person just as you used
automobile fuel consumption rates for understanding the health of your
automobile as well as anticipating your weekly/monthly/annual out-of-pocket
expenditures for gasoline or diesel fuel.
The failure rates drive the maintenance interventions, spare parts, and
maintenance cost for the maintenance department. Similarly they predict the interruptions to
the process and lead to misses on promised deliveries and result in negative
variances for production costs. In sort,
failure rates are precursors for the misery expected for the organization.
What: Fault tree analysis
(FTA) is a top-down process of defining the top-level problems and, through a
deductive approach using parallel and series combinations of possible
malfunctions, to find the root of the problem and correct it before the failure
occurs. The reliability tool can be used
as qualitative or quantitative methods.
Why: The tool aids
the design process, shows weak links that cause failures, and in the critical
legs of the trees, helps to define maintenance strategies for which pieces of
equipment and processes should be defended with the greatest maintenance vigor
to prevent “Murphy” from shutting down the process or causing serious safety
issues. The technique provides a graphical aid for the analysis and it allows
many failure modes including common-cause failures. Results from a FTA is usually more
pessimistic than other analysis tools such as RBDs as you
can see from a study of the Space Shuttle reliability analysis where each
system is studied by multiple reliability tools because of the high
cost/profile of failures.
When: FTA is widely
used in the design phase of nuclear power plants, subsea control and
distribution systems, and for oversight studies in layers of protection studies
for process safety and loss control in chemical plants and refineries so as to
prevent accidents and control the costs of risks. The technique is helpful for identifying
critical fault paths, observing vague failure combinations before they occur in
reality, comparing alternate designs for safety, and setting a methodology to
provide management with a tool to evaluate the overall hazards in a system and
avoid single sources of critical failures.
Finally when thinking top-down about failures and where/how they can
occur, the methodology gives a diagram for setting maintenance strategies for
protecting key pieces of equipment/processes to prevent failures.
Where: FTA is
helpful for defining potential event sequences and potential incidents,
evaluating the incident consequences of outcomes, and estimating the risks of
events occurring. FTAs
work in the design room and on the operating floor where firsthand knowledge
has been gained for preventing failures.
What: Failure mode
and effect analysis (FMEA) is the study of potential failures that might occur
in any part of a system to determine the probable effect of each failure on all
other parts of the system and on probable operations success. When criticality analysis is added for sophisticated
studies the method is know as FMECA. In
the automotive world where FMEA is a required portion of the quality systems,
it is frequently known as PFMEA for potential failure mode and effect
analysis. The basic thrust of the
analysis tool is to prevent failures using a simple and cost-effective analysis
that draws on the collective information of the team to find problems and
resolve them before they occur.
Why: The analysis
is known as a bottom-up (inductive) approach to finding each potential mode
of failure and preventing failures that might occur for every component of a
system. It also used for determining the
probable effects on system operation of each failure mode and, in turn, on
probable operational success, the results of which are ranked in order of
seriousness. FMEA can be performed from
different viewpoints such as safety, mission success, availability, repair
costs, failure modes, reliability reputation, production processes, follow-on
service, and so forth.
When: The FMEA is
most productive when performed during the design process to eliminate potential
failures. It can also be performed on
existing systems where operations personnel and maintainers are made team
members to add real-life experiences to educate the team in a problem-solving
forum that is constructive to eliminating existing problems.
Where: The analysis
can be conducted in the design room or on the shop floor and it is an excellent
tool for sharing experiences to make the team aware of details that are known
to one person but seldom shared with the team.
It is also an extremely productive tools for
educating young engineers, young maintainers, and young operators into details
they should be aware can kill the system.
What: Failure
reporting corrective action systems (FRACAS) is an organized database for
aiding in solving reliability problems using a common-sense approach by
systematically and permanently removing failure mechanism. Good historical data from this system can
populate a Weibull database.
Why: Use data to
solve problem by attacking root causes to reduce failures and make reliability
grow. Fixing failures requires data—not
opinions—so use the data acquisition system in a closed loop to record,
analyze, correct, and verify improvements have been achieved. First data reported is usually a symptom of a
failure and with a failure investigation, the symptom
can be converted into a root cause which requires the system to be editable to
correctly report failures.
When: The
maintenance repair order system usually generates evidence of a failure. Failures with significant costs (repair costs
+ collateral damage + lost margin from the failure + other appropriate business
costs) must be investigated and evaluated to reduce failures and to reduce
failure costs. Little is to be gained by
spending big money to investigate trivial failures.
Where: This is an
engineering tool requiring clerical effort to input the data and build the
Pareto distributions for identifying significant events requiring corrective
action and thus it also becomes a management tool for controlling costs.
What: Highly accelerated
life test (HALT) is an offspring of older environmental stress screening (ESS) tests and is a testing process
for ruggedization of pre-production products by
heavily stressing the product to identify failure modes quickly and to verify
weak links in the system.
Why: HALT tests
are intended to quickly find failures and accelerate the improvement program so
that when products are delivered to end users, they will be mature products by
elimination of potential failure modes that would normally generate a
reliability growth program. Usually the
HALT programs reduce time, cost, and delays experienced in new products by
recalls, warranty costs, etc. HALT is
similar to HASS but the stresses are more severe. In the HALT process, design and process flaws
are found, root causes identified, and corrective actions implemented quickly.
When: HALT is used
during the development program to get engineers to acknowledge and correct fatal
problems in designs by adding loads (generally temperature, vibrations,
pressures, physical stresses, etc.) by rapidly changing the load conditions
over and above normal operating loads.
Where: HALT is frequently
used for electronic systems but also applicable to mechanical systems where
thermal shocks are used to validate designs for extreme conditions of
loads. The tests are performed in the
laboratory for engineering evaluation.
What: Highly
accelerated stress screen (HASS) uses the same stresses as HALT,
but at a lower stress level. Compared to
HALT testing, temperature and voltage extremes may be reduced by 10%-15%,
vibration levels reduced 50%, etc., depending upon the design although all the
stresses may be above rated product specifications with the motivation to
produce test results quickly for verifying product compliance.
Why: HASS testing is used
to verify product performance is on
target and has not shifted toward inferior performance in the manufacturing
process. Note that higher stresses often
produce accelerated failures out of proportion to the increased stress applied.
When: Products are
periodically screened by HASS to verify no shifts have occurred in the
manufacturing process.
Where: HASS tests
are performed as a quality assurance test in manufacturing facilities to learn
what you don’t know about each product as it is faster than a simple burn-in
test. If 100% of the finished goods do
not receive HASS, as when only a percentage of the product is screened by HASS,
this is called a highly accelerated stress audit (HASA).
What: Life cycle
costs (LCC) are all costs associated with the acquisition and ownership of a
system over its full life. The usual
figure of merit is net present value (NPV).
Projects are considered most favorable for large positive NPVs. However for
many cost individual cases, decisions are made for the least negative NPVs. In all cases,
the default position for accounting is to know the NPV for making no change and
this is usually the last alternative for most people associated with change.
Why: The first
cost for capital equipment (acquisition) is between ½ and 1/20 of the total
lifetime cost! The first cost,
acquisition cost, is usually definable by a firm quotation and sustaining costs
must be estimated and put into the appropriate time slots for discounting to
obtain the NPV for the project life.
Typical values used in industry for LCC are: discount rate = 12%, tax
rate = 38%, and project life is usually between 10 and 20 years.
When: Life cycle
cost is usually calculated as an up-front decision-making effort either for
projects or for cost-reduction efforts.
I does not work well for doing the analysis after the project is
underway.
Where: LCC is the
business of investing money to make changes occur. The NPV values add the voice of investments
to technical decisions to work for the lowest long-term cost of ownership.
What: A measure of
use duration applicable to an item. For
example, the life units may be starts-stops, run hours, hot-cold cycles, distances
traveled, emergency starts or starts, shelf life, and other measurements that
motivate failures.
Why: Life is
consumed by usage of life units. Some
life units occur as a sum of the different cases, for example on a gas-turbine
aircraft engine, take-offs consume more life than landings or enroute conditions which requires a synthetic value for how
life is consumed on a mission. For a
land-based, heavy-duty gas turbine used in the generation of electrical power
the number of starts is not equivalent to hours of operation as other wear
mechanisms are involved; however, 1 trip cycle = 8 normal shutdown cycles and
thus decreases the time between required maintenance actions.
When: Development
of a life-consuming profile may be more important than the literal measurement
of an elapsed time to adequately measure consumption of life that in the end
will result in a failure.
Where: Life units
have different measures and must be considered to obtain the proper “common
denominator” for calculations.
What: For
reliability successes, loads must always be less than strengths. When loads are greater than strengths,
failures occur. The issue is determining
the probability of load-strength interference, which is a joint probability of
when loads exceed strengths. The loads
should include expected conditions plus the foolishness of people to violate
rules and overload equipment, plus the vagaries of Mother Nature to impose
unexpected static and dynamic loads from hurricanes, tornadoes, earthquakes,
wildfires, and so forth.
Why: Neither loads
nor strengths are unmovable point estimates, although most designers use point
values. Failures occur and reliability
terminates when loads exceed strengths.
When: Loads usually
increase over time (e.g., airplanes like people, gain weight over time from
accumulation of dirt and extra equipment), strength usually decrease over time
(small fatigue cracks appear with many cycles and load-bearing strengths decline).
Where: Bridges have
finite lives because of load-strength interactions, wings break off of
airplanes from fatigue, etc. A few
failures are dramatic but most failures sneak up from the unknown in a variety
of ways to cause loss of reliability. To
prevent loss of the system requires many physical inspections to learn what you
don’t know!
What: Lognormal
distributions are continuous life functions that have long tails to the right
(display positive skewness) in time or usage.
A lognormal distribution plotted on semi-log papers would appear as a
normal curve.
Why: The lognormal
distribution is a common competitor to the Weibull
distribution for life. However it is
adequate for 85%-95% of all repair times.
When: Lognormal
distributions are motivated by multiplicative (or proportional) events that
grow with time, like crack growth, molecular diffusion, and some wearout
problems.
Where: In the days
when plots had to be made by hand, it was the first widely used transform to
convert plotted data into straight lines.
Today it is simply one of an arsenal of probability tools used to obtain
good curve fits to data with multiplicative type events.
What: The measure
of the ability of an item to be retained in or restored to a specified
condition when maintenance is performed by personnel having specified skill
levels, using prescribed procedures and resources.
Why: Maintainability
measures the percent of maintenance jobs completed to a standard time for the
repair, with repair times for the task usually plotted on a lognormal
probability plot.
When: First you set
a standard repair time for the task, second you set a skills level, third you
measure how you’re doing against the standard.
Where: Applies to
major tasks where many repetitions are expected and where considerable time is
required.
What: All actions necessary,
both technical and administrative, for retaining an item in or restoring it to
a specified condition so it can perform a required function. The actions include servicing, repair,
modification, overhaul, inspection, reclamation, and restored condition
determination.
Why: Equipment
deteriorates because of entropy changes, because of errors both overt and
convert, and because of the use of incorrect procedures.
When: Maintenance
is generally routine and recurring.
Where: The effort
includes fault location, diagnosis, repair, test, adjustment, replacement,
administration, and overhauls wherever equipment is located.
What: A tactical
job for rapidly repairing equipment to operable conditions by studying
operating and repair manuals. Acquires
failure data and prepares maintenance plans of restoring equipment to operable
condition in a minimum amount of time.
Prepares general diagrams, charts, drawings, and spare parts
requirements for maintenance planners.
Makes recommendations for improving the repair cycle. Provides manning level forecast for
supervisors and estimates the duration of outages. Determines the cost advantages of
alternatives for developing action plans to comply with internal/external
customer demands for timely repairs of processes/equipment. The purpose of these activities is to restore
equipment to service in a timely manner.
Why: Facilitates speedy
repairs by providing maintenance technology above the craftsman level and up
to, but not including, reliability engineering principles.
When: Provides
expertise for more complicated maintenance tasks or when organization and
oversight is required and time is of the essence for fast repairs.
Where: Provides on-site
expertise to aid craftsmen to solve non-standard repairs without hands-on tool
contact. Maintenance engineers serve as
liaisons with reliability engineers.
Management’s
Role For Reliability-
What: Management
must display leadership for setting a course for reliability
under their watch. Too little
reliability results in many breakdowns, high maintenance costs, missed
production schedules, and unhappy customers.
Too much reliability results in high equipment cost, complicated and
expensive redundancies, excessive procedures, and excessive operating costs
along with happy customers for product delivery but unhappy customers because
of high cost products. You’ve got to get
it right for your particular situation.
No 4th quartile producer has demonstrated high reliability
production systems. Many 1st
and 2nd quartile producers have demonstrated high reliability
production systems.
Why: Management
gets what management wants. Management
must say what they want and want what they say.
Management must be consistent.
Their talk must match their walk to achieve failure free processes
which take into account the cost of
unreliability throughout the entire system.
Management usually expresses their overriding desires and philosophy
with policy statements as a method of
widely communicating intent to the workforce and making the direction a part of
the organization culture. Management
cannot espouse a reliability culture but only talk about fixing things faster
or grumbling only about maintenance costs—they must work to correct the root of
the failures and develop a culture of failure prevention.
When: Management
can adopt the reliability culture role at any time. The program has to be sold to the
organization—telling won’t implement an initiative for reliability. As a working example, follow the methodology
used for implementing strategies and policies for safety, quality, and
environment as role models.
Where: Management’s
role for reliability starts at the top as a strategy issue. It cannot begin at the bottom of the
organization.
What: A density
figure-of-merit metric often referred to as the average or expected value. In the simplest form it appears as arithmetic
S(time) / S(events) or in complicated situations as a statistic
metric. It applies to mean life (ML), mean down time (MDT), mean maintenance time (MMT), mean time between failures (MTBF for repairable items), mean time
to failures (MTTF for replacement
items), mean time between maintenance (MTBM),
mean time between maintenance scheduled (MTBMs), mean maintenance time
unscheduled (MMTu),
mean maintenance time scheduled (MMTs), mean time between overhauls (MTBO), mean time between unscheduled removals(MTBRu), mean time to restore (MTR), mean time between downing events
(MTBDE), and so forth. The units will be time/metric, e.g., hours/failure. The reciprocal of the metric provides an
incident rate, e.g., failures/hour.
Why: The metric
provides an awareness factor for deciding central tendency numbers and for the
expected number of events that will occur into the future based on historical
situations. The arithmetic simplicity of
mean time is a reason to establish the metric and listen to the information
derived from it to gain insight. The
arithmetic provides immediate answers to categorize facts for starting
continuous improvement rather than postponing a metric while searching for
delayed perfection!
When: The metrics
are used as criteria of performance and variations from the central tendency
numbers are expected however for the long term the variations are expected to
be controlled to prevent distortion of the measurement.
Where: The metrics
are used from the shop floor to the management levels as criteria for “How are
we doing?”.
Mechanical Components Interaction-
What: Mechanical components
suffer from interactions and degradations of overloads, strength deterioration,
wear, corrosion, process variations during the fabrication process, effects of
special processes where the procedures must be controlled as discovery of the
end results would result in destruction of the component, and removal of safety
factors by increasing loads.
Why: The naïve
expectation is that, individually, the impact of a single insult will not
destroy reliability of the component.
However, you frequently have multiple insults occurring, which results
in failures that are not predicted up front but can be perfectly explained
after the components have failed.
When: The multiple
destructive events are more predominate in complex devices and highly stressed
devices which too often have small safety factors that cannot cope with the
overload conditions and thus failures occur.
Where: The
foolishness of humans adds further insults to the interactions of many
different failure mechanisms which demands many more maintenance interventions
and frequent inspections. Of course the
solution to many of these cases where failures occur
is to increase safety factors by adding extra material (when possible), but
this adds extra weight and extra costs.
What:
Why: The technique
is used when: 1) many variables are present and their interrelationships are
unclear, 2) the system can’t be analyzed by direct and formal methods; 3) building
analytical models would be time consuming, complex, and just too hard, 4) you
cannot do direct experiments, 5) when the input details such as equipment life
and repair times are not discrete and they vary over time according to a
distribution, and 6) you need to do some tweaking of the system to understand
where opportunities lie for improving uptime, reliability, and costs.
When: Build models
before you commit systems to bricks and mortar so you know their performance on
paper. Revise the models after they are
in operation to help improve the unknown weaknesses and improve costs for
future cases.
Where:
What: A fundamental
frequency distribution that produces a symmetrical bell-shaped diagram based on
the Gaussian distribution to form a normal law of errors.
Why: The
distribution is easily described with two statistics, the mean (X-bar, which is
a location parameter) and the standard distribution (sigma, which is a shape
parameter carrying units of the location parameter) as these are parameters of
the population.
When: The distribution
is widely used for quality issues where errors are frequently symmetrically
distributed and for a few cases of reliability problems where life data is also
symmetrically distributed. For
symmetrical life data, the normal data makes a good Weibull
plot, whereas Weibull data usually makes a poor normal plot—thus, Weibull plots
have almost displaced normal plots for reliability data.
Where: The
distribution is used where the statistics simplify descriptions of the
distribution, so it is easy to describe and explain.
What: Overall
equipment effectiveness (OEE) is a manufacturing index to reduce complexity of
discrete systems for problem solving and benchmarking. In many ways, it is a subset of effectiveness.
OEE=availability*performance*quality where availability = (operating
time)/(planned production time), performance = (ideal
cycle time)/(operating time/total pieces), and quality = (good pieces)/(total
pieces); and OEE is best suited to discrete manufacturing. The index is larger than for effectiveness
and allows for acceptance of down time without have a hard measure for
utilization losses in the capability (although it does have a performance index
which takes elements from both efficiency and utilization) and it accepts
planned downtime as OK in the availability index. The effectiveness index looks at the system
from the perspective of the investor, whereas OEE looks at the system from the
perspective of the operations management which excuses many losses such as
planned outages, etc., and has the propensity for the indices to be “Enronized” so they look good, when in fact from the
investors viewpoint, the results are not good which is a violation of the
principle of Esse Quam Videri
(To be, rather than to seem).
Why: It’s a simple
and easy-to-use index for the big-picture summary of performance in industry
and it can be benchmarked against similar industries.
When: Use for a
quick assessment and approximation of the effectiveness equation.
Where: Widely used
for a first cut at improving manufacturing operations in lieu of the more
stringent and complete effectiveness equation.
What: Vilfredo Pareto was an Italian
economist in the late 1800s who described the unequal distribution of wealth in
the world. The concept was improved and
brought to the factory floor by Joseph M. Juran
(December 24, 1904-February 28, 2008) for manufacturing operations. Juran said it was a
methodology for separating the vital few problems from the trivial many
problems. The Pareto principle, as
explained by Juran, when applied to quality issues
said: It’s the 80-20 rule where 80% of
the problems come from 20% of the causes and management should concentrate on
the 20% (the vital few causes). The same
concept works for money issues—you must separate the vital few issues from the
trivial many issues.
When the Pareto distribution is listed in order of money lost
(including the risk for money lost) it becomes a work priority for attacking
business problems that have the greatest impact on the enterprise. Winners in the organization work on the vital
few important items (the 20%), as they put their reputations at stake, while
the losers in the organization work on the trivial many problems (the 80% of
the problem list), which, if solved, would have little financial impact on the
enterprise.
The gear-head approach is to build the Pareto list based on
numbers of failures. This is usually not
too productive. Would you really prefer
to solve 90% of
1) 1000 failures that costs a total
of $1000, or
2) 1 failure that costs $1,000,000?
The gear-head approach says to go for the 1000 small problems. However the business approach says to go for
the big $ items in the list—in the end, it’s all about the money!
The business approach is to build the Pareto list based on the
total amount of money spent or at risk (maintenance costs + gross margin lost +
rework costs + scrap costs + warranty costs + … + …., etc to include all
appropriate business costs) rather than working on the trivial money and love
affairs that keep people busy but do not generate financial returns for the
business.
The most important reliability tool is a Pareto
distribution based on $’s to set work priorities for attacking the vital few
problems as a method of separating important issues from the trivial many
issues.
Why: The Pareto
distribution, based on $’s, sets
work priorities, and assuming a one-year payback period, describes how much
money can be spent to resolve the issues.
Most reliability engineers need to be working on the top 5 or 6 items, based on $’s, all the time as data and
solutions are developed slowly and the key items always need to be on the mind
for active consideration. The mentality
is to think like a bank robber—go for where the big money is located and get it
back—and get it back fast.
When: At least
quarterly reviews of the Pareto distribution are important for accountability
of who has solved what problems and to define what new targets have come over
the horizon that require immediate attention.
Where: Pareto
distributions are used throughout the organization to keep attention on the
vital few $ issues. They are highly
favored by management when engineers employ Pareto distributions based on
money. Pareto distributions help set
work priorities and avoid focusing on love affairs with equipment or process,
which often occurs to the detriment of the business. Pareto distributions explain why some work
orders always get maintenance priority while other tasks are relegated to the
category of “whenever we get time to solve the problem.”
What: Poisson
distributions are discrete distributions and the simplest statistic process
where Poisson events are random in time, which describes a stable average rate
of occurrence of counted events. The
Poisson is frequently used as a first approximation to describe failures
expected with time. The calculations are
driven by an average value, e.g., failures/year, defects/meter2,
hurricanes/year, etc. Answers from the
Poisson will come as probabilities for 1 failure, 2 failures, etc., or the
probability for 1 hurricane in a year or 2 hurricanes in a year, etc. The average value is obtained from a
constant*time-interval that is usually explained as l*t. Frequently charts are used to obtain
solutions to the Poisson equation such as the Thorndike Chart from Bell Labs or
the Abernethy-Weber chart from The
New Weibull Handbook. The equation
is often described in two formats: 1) probability = (np)re-np/r! where n = number of trials, r = number of occurrences, and
p=probability of an occurrence, or 2) probability = ZCe-Z/C!
where Z=expected number (i.e., the mean) and
C=probability of an event in counting numbers.
Of course, for the two different formats np=Z
and r=C. When n is large and p (or 1-p)
is small, the Poisson is an excellent approximation to the binomial
distribution.
Why: Simplicity is
the major reason for use of the Poisson distribution.
When: Use the
Poisson when an answer is needed quickly and the answer deals with counting
terms.
Where: When you know
the average number of events the Poisson is easy to use to find the probability
of 1, 2, 3,…events occurring.
What: Probability
plots make sense of the chaos of failure data on an X-Y plot. Each type of plot is divided differently on
the X and Y axis based on the fundamental mathematics for a given
distribution. The decision on which type
of graph paper to use is based on: 1) a simple pragmatic approach (use the one
that gives the best curve fit to the data), and 2) the physics of failure or
the mechanism driving the data for non-failures. For reliability data, 85% to 95% of the data
will adequately fit a Weibull
distribution. For repair data, 85% to
95% of the data will adequately fit a lognormal
distribution. Often Weibull plots or
lognormal plots compete as to which distribution best fits the failure data.
Why: The acquired
data is plotted in the units acquired on the X-axis of a probability plot and
the data is plotted in rank order. The
Y-axis in most cases is determined using Benards median rank approximation to provided
the probability percentage. The result
is often a straight line on the properly divided X-Y graph paper. Please note, over the years many different
plotting positions have been tried with Benard’s
plot position being the strongest survivor for tailed (i.e., non-normal) data.
When: Use when you
have failure data or repair data. They
work best when age-failure plots are made by individual failure modes or
individual repair modes. They also will
handle high-level failure data and repair times where the data represent how
the system is behaving.
Where: Use
probability plots to get complicated data summarized onto one side of one sheet
of paper. When the plots have the
cumulative distribution plotted on the Y-axis, it tells what percent of the
population will have a life (or repair time) less than the corresponding
X-value.
What: Reliability
of a production process is defined as the percentage of production where output
consistency is lost as determined by a Weibull plot of daily production
output.
Reliability losses are the sum of production gaps between what should
have been demonstrated (the demonstrated production line) and what was
actually achieved—these are losses due
to special causes. Special cause losses occur from things you can put your
finger on and can be solved by process engineers, maintenance engineers, and
reliability engineers.
Nameplate lines (or entitlement line) define the possible daily
output. Nameplate lines lie to the right
of the demonstrated production line on a Weibull probability plot. The gap between the nameplate line and the
demonstrated line quantifies efficiency/utilization losses—these are losses due to common causes. Common cause losses result from subtle
problems without major identifiers and generally accepted as “that’s the way
things are” without fingering for elimination by six-sigma black-belts and
management. In many production
facilities, this category is a major source of losses and greater than all
availability/reliability/maintainability losses.
The sum of the reliability losses plus efficiency/utilization losses
constitutes a hidden
factory measured in output quantities.
Production effectiveness = (annual
output)/(annual output + hidden factory losses). These details are shown graphically on a
Weibull probability plot. Contrast the
production effectiveness calculation (obtained in minutes) to the effectiveness
equation (obtained in hours/weeks).
Why: You must see
the losses on a Weibull probability plot to believe they exist. Use the graphics to sell an improvement
program based on diagnosis of the problem and where to attack. The technique provides both visual and
qualitative results. The analysis goes
onto one side of one sheet of paper.
This is a simple tool used for strong results in a creative and problem
solving organization. Reliability values
and the slope of the demonstrated line (beta) are benchmark able. Process reliability techniques measure system
performance, in production output quantities, and produce a single production effectiveness index in
percentage terms which is similar to the effectiveness
equation.
When: Works well on
daily production data accumulated over a period of time in order to see the
patterns of performance.
Where: Useful for
any production facility including electrical power generation, chemical plants
(both batch and continuous process), refineries, pharmaceuticals,
semiconductors, packaging facilities, and other complicated production
facilities where achieving a simple index of “how are we doing” is difficult to
achieve. For more details and articles,
see hyperlinks at the bottom of the page: http://www.barringer1.com/prtraining.htm.
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Quality Function Deployment-
What: QFD is a bad
translation of a good reliability technique for getting the voice of the
customer into the design process so the product delivered is the product the
customer desires. In particular, it is
applicable to soft issues that are difficult to specify.
Why: The method
helps pinpoint: 1) what to do, 2) the best ways to accomplish the objective, 3)
the best order for achieving the design objectives, and 4) the staffing/assets
required to complete the task.
When: QFD is a
major up-front effort (as is the case with most Japanese techniques) to learn
and understand the customer’s requirements and the approach that will satisfy
their objectives.
Where: The
methodology is used as a team approach to solving problems and satisfying
customers, beginning with a listing of customer requirements, converting
customer requirements into engineering characteristics (the house of quality),
converting engineering characteristics into parts characteristics (the house of
parts deployment), converting parts characteristics in process characteristics
(the house of process planning), and finally, converting the process
characteristics into production characteristics (the house of production
planning). As with all Japanese
techniques, the up-front costs are high and many clever graphical tools exist
for transferring information with the intention of decreasing costs downstream
while satisfying customer’s needs.
What: Reliability is the
probability that a device, system, or process will perform its prescribed duty without failure for a given time
when operated correctly in a specified environment. This means that reliability is concerned with
the probability of future failures based on what has occurred with past
observations so we predict the future based on past observations.
Why: Reliability
has two broad ranges of meanings:
1) qualitatively—operating without failure for long periods of time just as the
advertisements for sale suggest, and
2) quantitatively—where life is predictable, long, and measurable in tests to
assure satisfactory field conditions are achieved to meet customer requirements.
Reliability is concerned with failure-free operation for periods of time,
whereas quality is concerned with avoiding non-conformances at a specified time
prior to shipment; thus, reliability measures a dynamic situation but quality
measures a static situation. As in
physics, statics (not time dependent as with quality issues) is easier to
understand and calculate than dynamics (time dependent as with reliability
issues), which involves higher levels of math and greater mental capabilities
for comprehension.
When: Reliability
is expected for new equipment to start, run, and continue to function for long
periods of time without failure.
Reliability is also expected when the equipment is dormant and called to
duty. Reliability is also expected upon
service or restoration and resumption of long life. Reliability is designed into the system by
up-front activities, and reliability is sustained by careful operation of the
system along with careful nurturing of the system with sustaining maintenance
activities. Reliability always
terminates in a failure and the roots of failure can be due to design,
fabrication, installation, operation, maintenance (repair and periodic
servicing), and management of the system—in short, there are many ways and
means to kill the system but few ways to keep is operating without failure.
Where: The adage
says “the proof of the pudding is in the eating,” and, for reliability, the
proof of the system is in the long failure-free interval. Reliability tools are used from stem to stern
to demonstrate high reliability (the absence of failures for long periods of
time) by use of many tools such as:
reliability acceptance test to
demonstrate long life;
reliability analysis to compute the
expected results;
reliability and maintainability the
mathematical tasks that predict the expected results from the elements;
reliability apportionment to
allocate life issues in a top-down manner to meet an overall reliability goal;
reliability assessment determines
the achieved level of reliability of an existing system using data gathered
during test or use;
reliability assurance implements
planned management and technical measures to provide confidence that a
reliability target is obtained and maintained;
reliability
block diagrams to graphically and mathematically calculate reliability
results prior to building a system;
reliability-centered maintenance is
the systematic approach to identify preventive support and service according to
a set of procedures to reduce and avoid failures;
reliability confidence limits
demonstrate the limits for reliability within a given confidence limit;
reliability control is the
coordination and direction of system dependability
through design activities and management planning;
reliability critical item
identification whereby failure significantly affects system safety/cost or
operational success or maintenance/logistics support costs;
reliability data is the basic
age-to-failure data as life unit
information relating to the time-to-failure when organized by probability distributions;
reliability degradation which incurs
loss of the failure-free performance due to poor workmanship or bad parts or
improper operation or abuse or inadequate maintenance;
reliability design practices are a series of trade-off-tools to meet or beat the design
specification for reliability;
reliability development/growth tests
are the evaluations to disclose deficiencies and verify corrective actions to
prevent reoccurrence of the failures to achieve the design specifications and
sustain reliability growth toward
longer times between failure;
reliability estimates are life
values used prior to statistical experimentation with the end products to make
predictions, or assessments, or stress analysis evaluations;
reliability function is the graphical
representation of life characteristics plotted against operating time;
reliability growth achievement is
the systematic improvements of a item/systems dependability by removing failure
mechanisms through corrective actions to eliminate deficiencies and flaws often
achieved by means of test-analyze and fix;
reliability growth models
(Crow-AMSAA) measures the reliability
growth by means of log-log plots of cumulative failures on the Y-axis and
cumulative time on the X-axis to demonstrate with statistics that failures are
coming more slowly and reliability goals have been achieved;
reliability guarantee is the
commitment by suppliers to provide a given meant time between replacements or
to maintenance and overhauls intervals for equipment;
reliability improvement is the
identification of failure modes and effects having a critical impact on the
system failure potential of the design along with the systematic removal of the
failures to produce long life without failures;
reliability index is the ratio of
the mean reliability level achieved to the acceptable level specified in the
design as a figure of merit;
reliability measurement is
failure-free endurance assessment activity for making decisions about reliability
and demonstrating compliance;
reliability mission is the mission
time for demonstrating failure-free performance;
reliability prediction is the
process of quantitatively assessing whether a proposed or existing deign meets
a specified life requirement;
reliability prediction functions
estimate the life characteristics for setting goals and evaluating the design
benchmarks and needs;
reliability prediction limitations describes the
shortcomings in life values by analytical methods;
reliability prediction requirements
describes life assumptions, environmental data, and failure rates for the
design;
reliability prediction summary is a report providing conclusions and recommendations based
upon an reliability assessment analysis;
reliability program are the
activities to organize and achieve a system to insure reliability goals are
achieved and deficient areas shored-up;
reliability program plan is the
formal written definition of the specific tasks to fulfill the reliability
requirements;
reliability qualification test (RQT)
is an evaluation conducted under specified conditions using items
representative of the approved product configuration;
reliability quantitative elements
are the life characteristics and factors considered in predicting and measuring
reliability performance;
reliability requirements are the
numerical values representing a specified failure-free life or dependability
performance characteristic;
reliability sequential tests are
evaluations of the number of failures and the time required to reach a decision
based on the accumulated results of the reliability tests;
reliability tasks describe the
activities required to achieve a reliability program;
reliability tests are the formal
evaluation to determine a product’s longevity for the failure-free interval or
stability relative to time/usage;
and finally,
reliability with repair is the
failure-free performance achieved by redundancy with permitted online repairs
without interrupting equipment operation.
What: Reliability
audits verify your reliability program is effective and find areas of weakness
for corrective action. They are
inquiries by factual examination of elements of the system with written objective
criteria for performance, beginning with an assessment of how management is
involved and are they effective in building a productive reliability program.
Why: Most
organizations know where they are strong.
On an objective basis, few organizations know where they are weak. Reliability audits are a
fact-finding exercises similar to financial and quality audits to ferret
out weaknesses for corrective action.
The questions to be answered are:
1) How well are
you doing what you promised against your reliability
policy?
2) How well is upper management doing
against company objectives for reliability?
3) How well are reliability plans,
systems, and procedures working?
4) How well are plans, systems, and
procedures being executed against the policy?
5) How well are productive efforts for
reliability working toward achieving the goals?
6) How well has the reliability system
been communicated to employees and are they committed to understanding and implementing
the improvements? and
7) Are financial objectives being met as
a result of ongoing reliability improvements? (Which is the main objective of
the audit—not just a rigid procedural/bureaucratic compliance to details).
When: Detailed annual audits should occur annually
with a follow-up to occur six month later to insure that corrective action has
been implemented. Without a six-month
deadline, few tasks will be completed because of procrastination.
Where: Audits are
needed for 1) reliability system management, 2) new techniques, technology,
developments, and controls, 3) supplier control (internal and external), 4)
process operation and control, 5) reliability data programs, 6) problem-solving
techniques, 7) control of reliability measurements, 8) human resources
involvement, 9) customer satisfaction assessment (internal/external), and 10)
software reliability (excluding Microsoft products used in the office
environment).
What: Reliability
block diagram (RBD) models are graphical representations of a calculation
methodology for reliability systems.
Why: The RBD
models allow calculation of system reliability based on knowing/assuming
failure details of the components, starting with the least component and
growing the model to the greatest system to predict performance from the
elements.
When: RBDs are used in upfront
designs as a performance parameter and after the system is constructed to
ferret out poorly performing blocks that limit the system performance.
Where: Frequently
used as a trade-off tool to search for the lowest long cost of ownership and to
help sell alternative courses of action for moderating the effects of
reliability issues or overcoming the poor performance by alternative designs
where the results can be calculated before building the system as the results
of the calculations provide knowledge about availability, maintenance
interventions required for failures, and the number of spare parts required to
sustain operations. For other
definitions see MIL-HDBK-338,
sections 4 and 6.
Reliability-Centered Maintenance-
What: Reliability-centered
maintenance (RCM) is a systematic
planning process used to determine the maintenance requirements for a
system. RCM expects the system has an
inherent reliability and maintenance requirements are imposed upon the baseline
of inherent safety and inherent reliability designed into the system (the
design sets the standard, it can be high, medium, or
low).
Why: RCM does what
is required to make sure the systems continue to do what the users want
done. If the excellent maintenance
programs demonstrate the lack of reliability expected, then the system must be
improved by design changes to physical assets or the manner in which the assets
are used.
When: RCM requires
a cultural change in both management and employees to “do maintenance by the
numbers”. This requires discipline in the
organization to perform the FMEAs
that drive the work process for maintenance and it also requires defining functional failures.
Where: RCM works
better in top-quartile manufacturers who have a disciplined work force and are
interested in achieving excellence in 1) safety, 2) operability, 3) reduced maintenance downtime by a disciplined approach
to the maintenance activities, 4) high uptimes, and 5) a reduction in
failures. Lacking one or more of the
five efforts at excellence generally results in a failed RCM program.
What: A strategic
job for preparing plans to reduce the failures and the cost of failures as a preventative
measure to reduce the cost of unreliability. Acquires failure data and analyzes the data
to quantify the financial impact and prepare long-term solutions to prevent
reoccurrences to improve reliability and uptime. Determines the cost advantages and proposes
alternatives for solving the problem and recommends the alternative with the lowest long-term cost of ownership. The purpose of these actions is to prevent failures.
Why: Prevents
future failures by working on medium- and long-term projects using technology
to solve the problems. As required,
provides technical assistance to maintenance engineers to aid their efforts for
quickly restoring equipment to service.
When: Provides
expertise for avoiding failures by means of a technical solution to reduce the
high-cost reliability problems on the Pareto
distribution.
Where: Provides
technical support and solutions for management on longer range problems, and as
required, supplies technical assistance to maintenance engineers for immediate
and difficult restoration projects as a liaison effort. Supports task improvements
to accomplish longer term objectives (think months and quarters), which will
result in smoother operations, at lower costs, without failures.
What: Reliability
growth models are important management concepts for making reliability visual
with simple displays. The simple log-log
plots of cumulative failures on the Y-axis against cumulative time on the
X-axis often make straight lines where the slope of the trend line is highly
significant for telling if failures are coming faster (b>1),
which is undesirable, slower (b<1), which is desirable, or without
improvement/deterioration (b=1), which usually drifts toward
undesirable results. The reliability
growth models are frequently called Crow-AMSSA plots in honor of Larry Crow’s
proof of why the charts work as described in MIL-HDBK-189
when he worked with AMSAA.
Why: Both
engineers and management must see reliability problems to fix them. The simple log-log plots make the models
visible. The task of the reliability
engineer is to put favorable cusps on the Crow-AMSAA trend lines to make
failures come more slowly and thus decrease the long-term cost of
ownership. If you’re doing your improvement
job correctly, you’ll never have many failures until you have a cusp.
When: The plots are
useful for development tasks (where they first were used) or to long-term
operations. They work for safety
programs, plant improvement programs, environmental programs, or for cost
problems. Use the plots as “show me,
don’t tell me,” how the projects are proceeding and the key metric in the form
of line slope is easy to understand and easy to communicate in less than 60
seconds.
Where: They are used
for technical development issues or for management reviews. A picture is worth a thousand words for
getting management’s attention for focusing on a problem. Likewise the charts are highly useful for
showing the reductions in failures that have occurred from making a desirable
and permanent fix.
What: Management
communicates with their staffs through important policy statements. Management policies are general and relate to
procedures and rules which are specific for implementing policies. Written statements of policy regarding
reliability are decisive documents about avoiding system failures in the same
way that safety policies address the need for absence of human injuries,
quality policies address the need for absence of product discrepancies, and
environmental policies address the need for avoiding spills and releases. Management needs to also say, by a policy
statement, a reliability policy that may read like this:
We
will build an economical and failure-free process that will operate for 5 years
between planned outages.
This statement will clearly communicate that failures to the process (which
is the money machine) are to be abhorred and avoided!
Why: Process failures
are clearly money issues because, when the process ceases to run, the company
has no income, thus process failures are to be abhorred for killing the money
machine.
When: Implementing
a policy before constructions of new facilities is important to use the policy
as design criteria. When implemented
with older facilities, the task is more difficult and old facilities may never
be able to comply with the objectives at a reasonable cost alternative.
Where: Responsibility
for implementing the policy lies with:
1) the chief operating officer must authorize the policy and ensure the policy
is applied throughout the operations under the administrative directive that
sets the guidelines for financial and engineering measures,
2) the engineering/R&D executives are responsible for ensuring the policy
is implemented by systems engineering, design engineering, project engineering,
pilot plant engineering, and test engineering,
3) the manufacturing executive is responsible for ensuring that the reliability
policy is carried out by the materials and procurement functions, industrial
engineering functions, manufacturing engineering functions, operations
functions, and maintenance functions,
4) the quality assurance executive is responsible for the dissemination of the
reliability policy, its annual review and auditing for compliance to the spirit
of the policy, and for making recommendations to the chief operating officer
concerning continued relevance, applicability, and effectiveness, and
5) the human resources executive is responsible for ensuring that all new
employees are indoctrinated into the purpose and implementation of the
reliability policy as a part of the operation’s mission, goals, and priorities.
What: Suppliers
have two strategies for testing: 1) test for success and/or 2) test for
failures. Reliability testing produces failures,
particularly when the tests are accelerated with extra loads, and this may be
troublesome to have in the records for future lawsuits. Thus, it is often to everyone’s advantage to
perform reliability test under code names to protect against the broad rules of
legal discovery.
Why: The
reliability tests will determine a product’s longevity and failure-free performance. This requires data recording and data
integrity. Plans must be set for how the
tests are to be conducted, loads to be handled, duration
of the tests, environmental conditions, operating modes, failure definitions,
and documentation for recording/analyzing the test data.
When: Reliability
test are usually run prior to release of the product for sale or after the
product has been released and troublesome failures appear in field applications
where no problems were expected.
Where: Laboratory
test are conducted in many cases but in other cases the data may simply come
from field use. Note the failures
induced require extra components that must be expected and budgeted along with
the extra costs for data acquisition/analysis.
What: For
inexpensive components and inexpensive tests, simultaneous tests involve many
components under test loads/conditions at the same time for the purpose of
quickly acquiring data and producing test analysis as the failures occur. In simultaneous testing, the suspensions
(censored data) become important details for use in the statistical
analysis. Most simultaneous tests are
accelerated to generate the data in a short period of time, although this
carries the risk of introducing unexpected failure modes (but this can also be
useful information for anticipating field failures).
Why: Conducting
analysis of the early test results, when only a few failures have occurred,
will give precursors as to passing/failing the longer-term tests. If the early test results look encouraging,
the larger test may be allowed to run to conclusion. However if early test results are
disappointing, the test may be abandoned without using all of the testing
budget so that remedial action can occur prior to completing the full-scale
planned test.
When: This testing
is usually conducted prior to release of products. However, a similar watch may be setup for
warranty repairs so as to anticipate the cost and extra supplies required to cope
with an unexpected failure that was not forecasted.
Where: This strategy
is appropriate for inexpensive components in the test laboratory. However, for warranty problems, the issues
are very appropriate for expensive components or assemblies.
What: Software does
not wear out but it does fail and most failures are due to specification errors
and code errors with only a few errors in copying or use. The only software repair is by reprogramming
and adding safety factors is almost impossible.
Software reliability improves by finding errors and fixing the errors
but estimating the number of errors that cause failures is extremely difficult
as many branches of software code may lie dormant and unused until special
events occur to make the latent failures obvious. Software failures are not often time related
but are more software code page dependent.
Software reliability is improved by extensive testing to disclose the
failures and then fixing them to repeat the test all over again to validate the
fix did not generate more failures and to continue the search of other latent
defects.
Why: More than 50%
of the software bugs (failures) occur from specifications with lesser amounts
of failures from system design and the coding process. This is due to the lack of visibility in the
software process along with problems from those specifying the requirements
with problem roots in ambiguities, inconsistencies, incomplete statements, and
lack of logical requirements. This
requires that both inputs and outputs for software must be specified in greater
detail than for mechanical, electrical, or system data to avoid the errors and
conflicts.
When: “Clean room”
software procedures are a technique for extracting details from the customers
so the programmers get the scope of the project and the input/output correct as
an up-front effort to reduce errors and wasted code. Acquiring the data is tedious, and roughly
80% of the software budget is spent get the details “right” before programming
commences.
Where: Disciplined
software specialists carefully work the plan up-front to reduce errors and
testing time. Undisciplined, so called
“neo-experts” want to see busyness in code writing up-front and thus their
software reliability is worse from not having a firm foundation from which to
work.
What: For expensive
components and expensive tests, sudden death tests involve a few components
that tie-up a test frame as they are heavily loaded under the same test
loads/conditions with several items being run at the same time. When one of the items fails, the entire test
frame is shut down so that you have 1 failure (this is the sudden death!) and
several suspensions because the unfailed units are survivors as the test is
halted until the test frame is loaded with new samples for resumption of the
life test. Opening the test frame
(instead of tying up the frame until all samples have failed) is cost
effective. If three units can be tested
simultaneously and the test is halted on the first failure, then perhaps we
will literally have only 4 failures and 8 suspensions for preparing the Weibull analysis.
Will the 4 sample + 8 suspension data set be different than if all 12
samples had been run to failure?—the answer is yes, they will be different, but will they be significantly
different?—the answer is no to the significant difference. So, as with simultaneous testing the suspensions (censored
data) become important details for use in the statistical analysis. Most sudden death tests are accelerated to
generate the data in a short period of time although this carries the risk of
introducing unexpected failure modes (but this can also be useful information
for anticipating field failures).
Why: Sudden death
testing is all about the economics and shorter elapsed time for results.
When: Sudden death
testing is used for product acceptance tests.
Where: It is a quick
test for many products and the ongoing test for production lots.
What: Total
productive maintenance (TPM) is a corporate-wide effort involving all employees
to fully use equipment to the maximum limit employing an equipment-oriented
management concept to reduce failures and increase utilization of equipment and
processes in a productive manner. TPM programs
are teamwork programs and require a corporate culture of teamwork devoid of us
vs. them issues. All employees are
expected to accept ownership of the equipment and processes to do many small
things all the time to ensure high levels of availability by eliminating
failures in the early stages with low-cost actions. The employees approach the process equipment
as owners rather than renters.
Why: Maximizing
equipment uptime with lower costs by all employees working to reduce the many
small incidents that lead to a failure
When: Major maintenance
tasks are handled by the craftsmen. Most
small tasks are handled by operators in a never-ending effort of cleaning,
lubricating, and tightening to find problems early when they can be solved
simply instead of letting the problem grow to a major issue.
Where: TPM is a
system-wide effort of providing care to the equipment rather than saying “it’s
not my job,” and “We’ve got to fill out the paperwork before ‘they’ can do
anything.” The technique makes good use
of the 5 human senses but technical details must be taught to the work force to
understand good from bad and when action must be taken along with what must be
done—this requires a sharing environment where the work team works for the
common good of higher performance. If
the culture is me, me, me, TPM will not work.
What: If you’ve got
one piece of failure data and nothing else, you’re a poor person without much
hope. If you’ve got one piece of failure
data and a Weibull database,
you’re a rich person with a map on the back of an envelope and a compass by
your side to get you out of the abysmal swamp of ignorance and
misunderstanding.
Why: The Weibayes technique uses your failure
data and past experience to make Weibull
analysis forecast about what you should expect into the future and in many
cases, given a hypothesis of worst-case/best-case a failure forecast can be generated.
When: Use the
technique when you lack specific details but you know something from your past
experience—often the past experience reduces errors of Weibull analysis. Use Weibayes
analysis to make sense out of emotional nonsense.
Where: Use the
technique to say something and point noses in the right direction rather than
playing the role of Chicken Little with the sky falling. Some data is better than no data in most
cases, and when you can keep your wits and everyone else is in panic mode, it
quiets the problem to allow reason to prevail.
What: Weibull
analysis is the tool of choice for most reliability engineers when they
consider what to do with age-to-failure data.
It uses the Weibull distribution which says mathematically that
reliability, R(t) = e-(t/h)^b where t is time, h is a scale factor known
as the characteristic life (most of the Weibull distributions have tailed data
and lack an easy way to describe central tendency as the mode≠median≠mean;
however, regardless of the b-values, which is a shape factor, all of
the cumulative distribution function values pass through the h value at 63.2% which
thus entitles it to be known as the single-point characteristic life).
Why: The Weibull
distribution is so frequently used for reliability analysis because one set of
math (based on the weakest link in the chain will cause failure) described
infant mortality, chance failures, and wear-out failures.
When: Use Weibull
analysis when you have age-to-failure data.
When you have age-to-failure data by
component, the analysis is very helpful because the b-values
will tell you the modes of failure which no other distribution will do! When you have age-to-failure by system, the b-values
have NO physical significance and the b-, h-values only explain how the system is
functioning—this means you loose significant information for problem solving.
Where: When in doubt,
use the Weibull distribution to analyze age-to-failure data. It works with test data. It works with field data. It works with warranty data. It works with accelerated testing data. The Weibull distribution is valid for ~85% to
95% of all life data, so play the odds and start with Weibull analysis. The major competing distribution for Weibull
analysis is the lognormal distribution. For additional information read The New Weibull Handbook, 5th
edition by Dr. Robert B. Abernethy and use the WinSMITH Weibull and WinSMITH Visual software for
analyzing the data (both software are bundled for a reduce price as SuperSMITH).
What: Starting with
Weibull analysis of component failures, the shape factor b
derived from the Weibull analysis provides an objective guide for selecting
repair strategies.
Why: Experience
has shown when shape factor beta is:
b < 1, failure
rates are declining with time as occurs with infant mortality failure modes. This condition provides a run to failure
strategy. Older components are better
than new components because the failure rate for the population is lower than
when new.
b ≈ 1, failure rates are
constant with time as occurs with chance failure modes. This condition provides a run to failure
strategy (or a run until the component failure mode changes to a wearout
failure mode). An old component is as
good as a new component.
b > 1, failure
rates are increasing with time as occurs with wearout failure modes. If the cost of failures in service is much
greater than the cost for a replacement, the component may have an optimum
replacement interval for timed replacements.
If the cost of failures in service is equal to or slightly larger than
for a replacement, the component many have a run to failure strategy.
Bottom line: You must know your Weibull failure modes and your costs to make a
good maintenance decision.
When: Collect data
from the FRACAS system. Perform a Weibull
analysis. Store the data in a Weibull database.
Use the Weibull facts for making fact based technical decisions.
Where: Weibull
corrective action is used by maintenance engineers and reliability
engineers. It is a useful tool for
understanding scatter in the data and provides guidance for taking the
appropriate corrective action.
What: The smartest
way to maintain a reliability database is in Weibull format and Weibull databases are
available. Seldom do you see Weibull
databases from vendors because they jealously protect their data for
proprietary reasons—they live/die financially from the Weibull database
information.
Why: The Weibull
databases simplify the complications of failure data into two statistical
values of great importance:
b tells you HOW things
fail, and
h tells you WHEN
things fail.
The results are key benchmark data that tell you how you’re doing.
When: Gather your
failure data and create your own database.
No one is going to give you their database because they put much sweat
and tears into cleaning up the data so it is useful. The data needs to be locally generated
because it tells you: 1) the life from the grade of equipment
you purchase, 2) it describes the grade of operation of the equipment—do you
operate it like 16-year-old teen agers or wise old men/women of 65?, 3) it
describes the grade of maintenance you use to renew its life, and 4) it tells
you management’s expectations for how to treat the system.
Where: The data starts
out as a silly exercise by maintenance to accumulate data with much ridicule
from the unknowledgeable about why are you spending so
much effort to build a Weibull database.
Then suddenly when adversity arises, it becomes everyone’s prized
possession. Remember the worlds of
Rudyard Kipling about plight of the English soldier: To paraphrase: In peacetime it’s Tommy this
and Tommy that, and Tommy get out of the way…but you let the bullets fly in
wartime and it’s Mr. This and Mr. That and Mr., if you please! Everyone wants the baby but no one wants the
dirty diapers that go with every baby!
If you don’t have a Weibull database, you’re already too late because
your competitor has one started and is using it to
your disadvantage, and he’s not going to tell you why you’re left in the dirt!
Comments:
Refer to the caveats on the Problem
Of The Month Page about the limitations of the following solution.
Maybe you have a better idea on how to solve the problem. Maybe you find where
I've screwed up the solution and you can point out my errors as you check my
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