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Mean Time Between Failure or MTBF is almost universally recognized in maintenance, reliability, and asset management.
Why is that? And does it actually help an organization improve the reliability of their assets?
The answer is a resounding No.
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You may already know my position on MTBF.
If not, do not use MTBF at all, ever, in any form. So what should we use instead?
I suggest using reliability, the probability of success over a specific duration.
Let’s discuss what will work for you.
[Read more…]You may already know my position on MTBF.
If not, do not use MTBF at all, ever, in any form. So what should we use instead?
I suggest using reliability, the probability of success over a specific duration.
Let’s discuss what will work for you.
As with other point estimates, we often want to calculate the confidence interval about the estimate. The intent is to determine the range of reasonable values for the true and unknown population parameter. For MTBF, this no different.
Here’s Fred on a Dare to Know podcast episode talking about the book and blogging about the issues and solutions concerning MTBF. You can learn more about Fred at his bio.
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. [Read more…]
Start the discussion. Encourage those around you to consider why and how they are using MTBF.
At one point, someone noticed I was pretty adamant around this topic and after mentioning it was my personal mission or I was mounting a campaign to get rid of MTBF, they suggested creating campaign buttons. Those buttons have now evolved into a full fledge store. Get your t-shirts, jackets, earrings, coffee mug, and, of course, buttons at the CafePress NoMTBF store.
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A one-sample t-test is a statistical test to determine if the sample mean differs significantly from a known or hypothesized population mean (expected value). It is commonly used when we have a single sample and want to compare its average to an expected value.
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In this video, I’ll walk you through the essentials of preventive maintenance – what it is and how to correctly identify the intervals for preventive maintenance tasks. We’ll talk about scheduled replacement and scheduled restoration tasks, and I’ll take you along as I perform three real-life examples: changing my home HVAC filter, getting my hair colored, and an oil change for my Subaru Forester. Each task highlights the importance of scheduling based on “age,” regardless of the condition at the time.
I also explain why preventive maintenance intervals should be set based on the useful life and not manufacturer recommendations, mean time between failures (MTBF), or criticality. Understanding these concepts is key to building a strong Reliability Culture in any organization. If you have any questions about preventive maintenance, drop them in the comments. I’m here to help!
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This is a question someone posted to Quora and the system prompted me to answer it, which I did.
This question is part of the general question around which software tools do you use for specific situations. First, my response to the question. [Read more…]
A result of life testing can be measurement or evaluation of the lifetime.
Measurement of the lifetime requires a lot of testing to failure. The results provide us with the life (time-to-failure) distribution of the product itself. It is long and expensive.
Evaluation of the lifetime does not require as many test samples and these tests can be without failures. It is faster and cheaper [1]. A drawback of the evaluation is that it does not give us the lifetime distribution. The evaluation checks the lower bound of reliability only, and interpretation of the results depends on the method of evaluation (the number of samples, test conditions, and the test time). [Read more…]
How can we tell whether an iron is hot enough? The answer is obvious: We can measure temperature by using a thermocouple and a meter. But, in practice, we lick our finger and touch the iron. Sizzle…. Yes, it’s hot!
We know a priori the boiling temperature of water and we can evaluate the temperature of the iron. This method has a lower cost. [Read more…]
Insert these pages into your copy of MIL-HDBK-217. The boldface text is changed to MIL-HDBK-217E [1], section 5.2, on parts count reliability prediction. The changes explain how to use “Paretos,” proportions of parts failing in the field, to compute a reality factor that makes predicted Paretos match field Paretos. You can use field Paretos to calibrate predictions for new equipment. You probably have field Paretos on related parts used in your other equipment, which is now in the field. Remember, the field determines reliability.
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