How to get peaks back to 120 G in random profile?
by John Van Baren
The problem:
Here is the real life scenario often encountered: In the automotive world, lots of data is taken from
vehicles on the test track. Sound, temperature, vibration, engine rpm, etc. To measure vibration,
accelerometers are placed on many locations of a vehicle. Then, the vehicle is driven around a prescribed
test track, with many road surfaces. Simulating the real environment. Now, an engineer has all this time
history data, and needs to define a random vibration test for a particular component of a vehicle. Perhaps
an oxygen sensor, for example. The obvious solution is to take the accelerometer data from the accelerometer
mounted by the oxygen sensor, run this data into a spectrum analyzer, and read the PSD. Simply use this PSD
to define the random test. When looking at the resulting PSD, and comparing it to the actual data, often a
discrepancy is noted. In the actual data, the engineer may see 120 G peaks, even in the frequency band-limited
actual data. But, the random test PSD perhaps gives a 15 G RMS test. We are pretty sure we will only see 3 or
4 times the G RMS level (3 to 5 sigma peaks) when running the random test for the duration of 2 hours. Even
with the sigma clipping turned off. This is only 45 to 75 G peaks.
The question is: How do I get the peak levels back up to 120 G peaks when running my random profile? There are several
solutions implemented in the real world to "solve" this issue.
The first, and most common solution I have seen, is "recognize and complain". It involves simply observing that the
real world data contains 120 G peaks, and the random test never produces any peaks greater than about 50 G with any
regularity. "Why do we have to run this test? It never produces any failures anyway" is the statement made by the test
engineer.
The second most common solution I have seen is "recognize and adjust". This solution approach involves the clever test
engineer who recognizes the lack of peaks in the random test, and the desire to fix the problem. The solution is to raise
the G RMS level (and spectrum level) to get the peaks back into the test. Usually the number 4 is used. If you want 120 G
peaks, divide by 4, and select 30 G RMS as the test level. Even though the RMS level is twice the desired 15 G RMS level,
at least you will get your 120 G peaks with some regularity. "We are testing this part to some very high levels, but we
feel comfortable that if it passes this test, it will survive the real world environment" is the statement made by this
test engineer.
The third solution is to "recognize and correct". This solution approach involves using emerging technology to re-shape
the probability density function (PDF) to fit the real world data. Rather than using the Gaussian distribution, adjust the
shape so you get 15 G RMS with 120 G peaks at regular intervals. Technology available today allows you to shape the PDF to
get 8 sigma peaks (120 G peak/15 G RMS)at the same repetition rate as Gaussian produced 4 sigma peaks.
Technology today can produce the peaks at all frequencies of the spectrum, and allows a much more accurate
representation of real world data for random testing. "We are testing the part at both G RMS and G peak levels really
seen in actual use" is the statement made by this test engineer.
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