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Load-Strength
Interference |
Loads vary, strengths vary, and reliability usually declines for mechanical systems. The cause of failures is a load-strength interference problem frequently describing mechanical systems which go bump in the middle of the night. Failure of mechanical systems “made the same” as other systems, invokes the old Cornish prayer:
“From ghoulies and
ghosties and long-leggety
beasties and
things that go bump in the night—Good Lord deliver us!”
Bumps in the night occur when loads are higher than strengths, or strengths are lower than the loads. So it might be a good idea to work out these details with facts rather than praying for help from the Lord to cover our ignorance.
Things don’t fail on the basis of averages (assuming average loads are widely separated from average strengths), parts fail on high loads/weak strengths. To get your thinking adjusted to this concept, people don’t usually drown in average depths in a river, they drown in deep water!
Seldom is the load fixed. When loads vary to the low side everything is failure free. When loads vary to the high side, failures occur and reliability is lost.
Seldom is the strength fixed. When strength varies to the low side, often because of lack of homogenous substances, failures occur and thus reliability is lost. When strengths vary to the high side everything is failures free.
The condition of unreliability is described in Figure 1. Loads and strengths interfere in the overlap area and this is the area of concern for failures.
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Figure 1—Load-Strength Interference |
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The overlap of load-strength in Figure 1 is not literally the calculated area. It’s a joint probability of occurrence problem or roughly ½ of the literal area. The joint probabilities are described by a double integral for reliability. If large safety margins are used, the probability of incurring failure from load-strength interference is usually very low when both loads and strengths are well known. If low safety margins are used, ignorance abounds for of load distributions, or ignorance of strength distributions, it’s another sad story. Failures occur where loads/strengths overlap.
O’Connor (see Practical Reliability Engineering, by Patrick D. T. O’Connor, John Wiley, 2002, ISBN 0-470-84462-0, Chapter 4 Load-strength Interference, page 114-129) points out that where loading roughness is low (i.e. small standard deviation of loads) and strengths are well behaved (i.e., small standard deviations of strengths) and displaced widely to the right of the loads you can achieve intrinsic reliability where the probability of failure is low when large safety margins of 3-5 are used. If the load curve is skewed to the right and the strength curve is skewed to the left, then larger safety margins are required for high reliability. In short, you need to know the load curves and the strength curves and keep them widely separated to achieve high reliability by avoiding load-strength interference. Safety margins of 3-5 are suitable for pressure vessels but not airplanes—so keep in mind the class of equipment you’re designing/maintaining and use the appropriate strategies.
Reliability of the component can be determined as the probability of load being less than the strength for all possible values of strength (see Reliability-Based Design, by S. S. Rao, McGraw-Hill, Inc., 1992, ISBN 0-07-051192-6, Chapter 8: Strength Based Reliability and Interference Theory, page 235-273). Rao’s equations of importance are:
ßEquivalent
to Rao’s Eq. 8.9
Where fS(s) is the probability density function of the strength, fL(l) is the probability density function of the load, and FL(s) is the cumulative distribution function of the load in units of the strength. The reliability statement is a statement of success.
The statement of unreliability
ßEquivalent
to Rao’s Equation 8.13
Where fL(s) is the probability density function of the load in strength units and FS(s) is the cumulative distribution function of the load in strength units.
You have three obvious ways to solve this
complicated and convoluted problem:
1) Use Mathcad to solve
the integral (download the mathcad ZIPPED file.
2) Use WinSMITH Weibull to solve the
problem using
(download the demonstration program to solve
the problem click on the calculator
icon, click on load-strength
interference and input the statistical data).
The
methodology for the simulation
is described below for the Excel simulation.
3) Use Excel to solve the problem
(download a Monte
Carlo simulation). The
simulation draws a random load
and a random strength from the described
distribution. If the strength is greater than the load you
have a success. If
the random load exceeds the
random strength, you have a failure. The
calculation for reliability is
R = (successes)/(successes + failures).
With the Mathcad file and the Excel simulation file,
you can see the equations used for the probability density function and
cumulative distribution function.
How do you find the correct statistical distributions to use for the load
analysis?
1) Measure and record the loads over
time. Treat the data as samples. Construct a probability distribution in
WinSMITH Weibull. Use good common sense
and good engineering judgment to select the appropriate distribution. The distribution will allow you to predict
loads above/below the actual data recorded when you treat the data as a sample.
2) Measure the strengths for many
samples as described above for the loads.
If you have many data, you may find strength data displays a
failure-free zone. The strength phenomena occurs with offset of the origin of the
distribution. This is described in The New Weibull Handbook as a t0
shift for a 3-parameter distribution.
Dr. Abernethy in The New Weibull Handbook sets four requirements for use
of a 3-parameter distribution:
1. More than 21 data
points are required for a valid analysis
2. Raw data plotted on a 2-parameter probability
plot will show a concave
downward appearance.
3. The goodness of fit criteria (i.e., R2
or PVE%) must show substantial
improvement with use of a 3-parameter
distribution compared to the
2-parameter curve fit.
4. A physical explanation of the reason for
shifting the origin of the
distribution must be obvious. For example, if the yield strength of the
steel grade is 110,000 psi,
then no steel is released by the steel mill
if it’s strength
is less than 110,000 psi. Thus yield strengths when
plotted on a 2-parameter probability plot
display a concave downward
appearance but will show a straight line
on a 3-parameter plot when
when 110,000 psi
is subtracted from the raw data to reposition the data--
be careful with data in the t0
as the numbers can be misleading unless all
details are kept in the datum as recorded.
By the way, many data collected often are recorded in convenient units. Convenient units often results in stacks of
data on a probability plot. This
requires the use of special regression techniques to achieve the correct
statistics and the method can be selected in WinSMITH Weibull under the methods
icon.
Loads and strengths are often described by the following common
distributions:
1.
2. Weibull (Weak links in the
chain failures)
3. Lognormal (Events accelerate with many small
data and some large data)
4. Gumbel smaller distribution
(Where small data are of concern and
recorded)
5. Gumbel upper distribution (Where large data are of concern and recorded)
Other distributions can be envisioned and used when appropriate but these
mentioned distributions will cover 95+% of the situations. Use common sense and good engineering
judgment in selecting the distributions.
Make sure the distributions display reasonable graphics for
comprehension by the engineering community.
The comprehension criterion for engineers is simple: No graphics—No comprehension! The equations for each distribution are shown
in Figure 2. The PDF is the probability
density function which has an area under the curve of unity and shows the shape
of the curve you would get if you made a tally sheet of occurrences on the
Y-axis versus the unit of measure on the X-axis. The CDF is the cumulative distribution
frequency and integrates the area under the PDF (which is then subtracted from
unity to predict the % of the population that will occur on the Y-axis versus
the unit of measure on the X-axis.
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Figure 2: PDF and CDF Equations |
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Sometimes the problems are difficult to solve in closed form solutions (where a failure free interval exists). Others are difficult to solve where the probability of failure is very low and a very large number of iterations must be run to get an answer that is not zero.
Consider the gust loads in Figure 3. The X-axis is give in g-loads. The data recorded was the maximum positive g-load from each of 23 flights. The plot as made in WinSMITH Weibull software.
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Figure 3: Gust Loads Described With
A Gumbel Upper Distribution |
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The PDF curve of the load is described in Figure 4. The data was generated from WinSMITH Weibull and plotted in WinSMITH Visual. Notice the long tail to the right with this actual data from a 1954 NACA document.
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Figure 4: Gust Loads As A PDF |
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Strengths were obtained and plotted in a Weibull plot in Figure 5. Again, note the X-axis is shown in g-loads and the failure free interval is 2.939.
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Figure 5: 3-Parameter Weibull
Strength Plot |
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This strength curves is selected on purpose because of it’s calculation difficulties.
The PDF for Figure 5 is shown in Figure 6.
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Figure 6: Weibull 3-Parameter PDF |
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The calculations and difficulties are shown in Figure 7.
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Figure 7: Mathcad
Load-Strength Interference Calculations |
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Use good engineering judgment and practical experience in interpreting the answer, and take some of the results with a grain of salt. By the way, if the structure only has a 2-Parameter Weibull plot for strength, you will get a significantly different probability for failure so strength distributions are very important!
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Last revised 01/11/2007