Calculating MTBF from data

There are occasions when we have either field or test data that includes the duration of operation and whether or not the unit failed. This can be, say, 10 large motors. For sake of argument, the test ran each motor for 1,000 hours and when a motor failed it was repaired quickly and returned to the test. There were 3 failures.

Sadly, this is all we need to calculate an estimate for the motor MTBF.

Total time divided by number of failures in this case is  10 times 1,000 hours for a total time of 10,000 hours. Divide 10,000 by the three failures to find, 3,333 hr MTBF.

What I find interesting is I could find the same MTBF value using 10,000 motors each run for one hour. Or, the same MTBF if we ran one motor for 10,000 hours. IF in each case there were three failures we would find the MTBF of 3,333 hours.

Now that works perfectly well when there is a constant failure rate. Meaning there is equal chance of failure each hour of operation. Old motors would have the same chance of failure as brand new motors.

Of course, you know why I choose motors for the example. To reinforce the idea that the chance of failure is not always a constant. Be sure to think about the failure mechanisms before using MTBF (or MTTF). If the failure rate is time dependent then this simple calculation is not useful.

I used this example during a class last week and it seemed to spark a good discussion. How have you explained MTBF to others? Any suggestions on how to best describe what the MTBF value really means, or doesn’t mean?

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About Fred Schenkelberg

I am an experienced reliability engineering and management consultant with my firm FMS Reliability. My passion is working with teams to create cost-effective reliability programs that solve problems, create durable and reliable products, increase customer satisfaction, and reduce warranty costs.

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