Is there a standard method for converting failures per million miles to failures per million hours in order to integrate a known value (i.e MMBF = 1500 miles) into a system level prediction (MTBF)?
The conversion factors that RAC uses in the analysis of data for NPRD-95(Non-electronic part reliability database)is 28.8 miles per hour for wheel vehicles and 10.0 for track vehicles. These factors are used to convert the total miles to hours, then statictical analysis is used to determine the failure rate at some confidence level.
Converting MTBF = MMBF/28.8 miles per hour is the standard, correct conversion, as long as all vehicles go exactly 28.8 mph all the time they operate.
If velocity is random, the conversion requires:
1. Probability distribution of velocity, e.g., P[MPH > z] for reasonable z values. This requires data.
2. Probability distribution of miles at failure, typically assumed to be exponential in MTBF prediction,
P[Miles to failure > x] = exp[-x/MMBF]
3. Probability calculus to compute the probability distribution of MBF/MPH and then integration to get
MTBF = E[TBF] = E[MBF/MPH],
where both MBF (miles to failure) and MPH are random variables.
A method for transforming reliability units is in “Convert Calendar Time to Operating Time Reliability,” Reliability Review, ASQ-RD, Vol. 18, No. 3, Sept. 1998, in a section entitled “CAUTION NEUROHAZARD! PROBABILITY AHEAD! CONVERT TO OPERATING TIME.”
If you dare risk the neurohazard, send an email to email@example.com and ask for the paper. Free samples are available. Send MPH and MBF data, and Larry will send back the MTBF, free.
I would like to convert MMBF in MTBF for a component used in an aircraft flying at 414 mph.
I would like to be sure that the following formula could be used:
MTBF = MMBF/414 miles per hour
Is there a “speed limitation” in the use of the formula or could I always use this formula to convert MMBF in MTBF?
Thanks for your answer.
There is no inherent speed limitation in using formula. If your MMBF data is accurate, then it should also be accurate when converted and measured on a time scale.