Category Archives: MTBF

Mean Time Between Failures or MTBF is a common metric for reliability and is often misused or misunderstood.

Grundfos MTBF Policy

A few months ago at a IEC Dependability standards meeting, I met Thomas Young Olesen of Grundfos and we talked a little about NoMTBF. He said their company has a polity to not use MTBF. YES! So I asked for permission to post some information about the policy.

One interesting part of their internal site was a MTBF Calculator. Continue reading Grundfos MTBF Policy

Lower Confidence

Let’s say we have a population and we are interested in the mean (average) of that population’s life. We select a sample (at random if at all possible) and measure a value, like time to failure, for each selected item in the sample.

We calculate the mean life of the sample by summing the sample values and dividing by the number of items in the sample. Continue reading Lower Confidence

MTBF and preventative maintenance

I find the world of maintenance a very odd place to find MTBF. While it is possible, that a set of equipment or a machine may actually have a constant failure rate it is the exception rather than all that common. Assuming a constant failure rate doesn’t make it so. Continue reading MTBF and preventative maintenance

MTBF Logic

The reason so many use MTBF is because so many use MTBF. ‘Our data sheet has to include MTBF since all the other data sheets have MTBF’. Which seems to be primary reason MTBF is so common. It’s because it is so common.

Against this logic is the desire I have to use a measure of reliability that actually is understood. Using reliability (probability of success over a specified duration) as a measure seems some how odd or novel. It is easy to understand and it doesn’t obscure the reliability. Continue reading MTBF Logic

Sample size and MTBF

Samples for Testing

Normally, we life test a sample of products in order to make sure the products will last as long as expected. We assume that the sample we select will represent the total population of products that we eventually ship. It is not a perfect system, and there is some risk involved. Continue reading Sample size and MTBF

No MTBF Tribe

Thanks to Kirk and the folks at TED for sharing another interesting presentation. Seth Godin: The Tribes we lead is a look at how we each can lead a movement to make real change in this world. Tribes and the organization of tribes has been around a long time, in recent years though those that controlled the mass media tried a different way to influence. Continue reading No MTBF Tribe

Marketing and MTBF

A few weeks ago I was working on a report summary that included a reliability value at 5 years for a product. The document was intended for use with customers, so it was reviewed by Marketing.

Not too many changes, which was nice as I do not consider myself a writer and certainly not a marketeer. They did ask for one change that prompts this note. They wanted the 96% reliable at 5 years to be replaced with the (roughly) 2,100,000 hour MTBF value instead. Continue reading Marketing and MTBF

The MTBF Battle Continues

This site is part a long string of attempts to eradicate the improper use of MTBF. This week two people have sent me references to work previously done and Chris sent me another podcast also highlighting issues with MTBF. Jim McLinn wrote about the possible transition away from constant failure rate Continue reading The MTBF Battle Continues

Wrong Conclusions

Here is a podcast by Chris Peterson of H and H Environmental Systems which includes her thoughts on the MTBF topic. She also explores how making even ‘obvious’ assumptions may lead to the wrong conclusions.

Chris records a podcast almost everyday and many are enjoyable, fun, and and provide something to think about as you go about your day. If you like the podcast above, check out her growing list of available podcasts.

Embedded podcast with permission of Chris.

Use Lognormal Distribution

The lognormal distribution has two parameters, μ and σ. These are not the same as mean and standard deviation, which is the subject of another post, yet they do describe the distribution, including the reliability function.

\displaystyle R(t)=1-\Phi \left( \frac{\ln (t)-\mu }{\sigma } \right)

Where Φ is the standard normal cumulative distribution function, and t is time. Continue reading Use Lognormal Distribution