A colleague and friend, Bill Barto, worked up an example concerning two pieces of equipment. By simply not assuming a constant failure rate the …. well, let Bill tell the story….
Here’s the story in Bill’s own words via Youtube.
[just in case you not able to view the video – here’s the text.]
Imagine you show up to a client site where there are two similar pieces of equipment that they are having trouble with. They want your help in determining where to start since they only have the resources to address one of the machines. They indicate that they have been tracking MTBF for the last two months on the equipment. They give you the following three pieces of information:
- Both pieces of equipment have been run for the exact same amount of time over the last two months and produce the same product at the same rate.
- Machine #1 has a MTBF of 25 hours for the first month and 46 hours for the second month.
- Machine #2 has a MTBF of 30 hours for the first month and 50 hours for the second month.
Which machine do you choose to work on?
From this information, it seems like Machine #1 is running worse than Machine #2 since Machine #1 has a lower MTBF each month. With this data, you may choose to focus on Machine #1 first since you would assume there have been more failures there given that they were run for the same amount of time over the last two months.
Here’s the kicker. When you ask them for more detail on where they got these numbers you are given the actual run times and number of failures.
[table width=”600″ colwidth=”10|170|10|170|10|170|10|” colalign=”center|center|center|center|center|center|center”]
Machine,Month 1,MTBF,Month 2,MTBF,Both Months Combined,MTBF,
#1, Run time = 150,,Run time = 690,,Run time = 840,,
,# of failures = 6,25,# of failures = 15,46,# of failures = 21,40
#2, Run time = 540,,Run time = 300,,Run time = 840,,
,# of failures = 18,30,# of failures = 6,50,# of failures = 24,35
Now when you compare the MTBF and number of failures for the combined time in the last set of columns, Machine #1 looks to be running better than Machine #2.
The method of calculating MTBF used here (run time / # of failures) is based on the assumption that the data is from an asset experiencing random failures. Recent studies have shown that failure rates that were assumed to be random (e.g., lightbulb failures) are not random at all. Because of the ease of calculation this technique is misused often. A better method might be to more closely look at the data (individual failure times) and determine the actual failure distribution (Weibull, Normal, Lognormal, etc.) through other methods. There are a variety of software tools for determining the best fit distribution so that metrics such as MTBF and others can be determined more accurately.
Bill Barto, CMRP, ASQ CRE
Life Cycle Engineering, Inc.
Thanks Bill for the story and case. It’s clear in nearly any situation where the MTBF values change from month to month that further analysis is well worth the effort. And, as I would say, it’s not much more effort and well worth letting your data speak clearly.