Your Reliability Engineering Professional Development Site
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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I searched the Internet for “Why Use Weibull Reliability?” and got 196 hits “similar sites omitted”. Some hits looked like they were generated by Chat GPT. None showed data. None told how to test or use Weibull reliability estimates. Many sell Weibull software. All require lifetime data.
Assuming a continuous, parametric distribution such as Weibull conveniently extrapolates limited, censored lifetimes to estimate MTBF, even though MTBF may be far in the future. Https://en.wikipedia.org/wiki/Weibull_distribution lists many uses for Weibull distributions. A client had me use Weibull to model durations of covered calls!
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If you sort your Amazon search on ‘reliability engineering’ by price: low to high, you may find some interesting titles available for free or maybe a few pennies. Not one to resist a chance to fill another bookcase, it’s been a bit of a spending spree.
One of the reasons I am interested in older titles is to determine why MTBF is so prevalent today. So far, still looking and learning along the way.
There are many great books in our field. Sure, some are older. Some are not at all useful or helpful.
This book review is the first in what may become a monthly addition to the NoMTBF blog.
Today’s review is on the book, An Elementary Guide to Reliability (3rd) Third Edition
, by G. W. A. Dummer and R. C. Winton. [Read more…]
One technique to calculate a product’s MTBF is to count the number of failures and divide by the tally of operating time.
You already know, kind reader, that using MTBF has its own perils, yet it is done. We do not have to look very far to see someone estimating or calculating MTBF, as if it were a useful representation of reliability… alas, I digress.
Counting failures would appear to be an easy task. It apparently is not. [Read more…]
Summary: In this episode of CMMSradio, Jennifer Dodd Fuller, Senior Manager of Industrial Energy Solutions at Ndustrial, shares insights into her career journey and experiences in industrial maintenance and reliability engineering. Starting with a background in HVAC, Jennifer transitioned into reliability engineering at Owens Corning, where she learned the value of collaboration, process improvement, and proactive maintenance. She discusses the role of CMMS in improving maintenance practices, advocating for intuitive systems that are user-friendly for technicians and highlighting the importance of data-driven strategies to demonstrate success. Jennifer also addresses the challenges of balancing reactive and proactive maintenance and the need for leadership support in using CMMS data to enhance team performance.
Takeaways:
Nearly everyone I’ve ever met doesn’t want or like their toaster to fail.
It will, and that is a bummer, as the quick and easy way to warm up the morning toast will be thwarted.
Failures happen. As reliability engineers, we know that failures happen. Helping others to identify potential failures, to avoid failures or to minimize failures is what we do best.
It is our ability to teach others about reliability engineering that allows us to be successful. [Read more…]
High equipment reliability is a choice and not an accident of fortune. To a great extent you can choose how long you want between equipment failures. You can deliver high equipment reliability by ensuring the chance of incidents that cause failures of equipment parts are low. The secret to remarkably long and trouble-free equipment lives is to keep parts and components at low stress, within good local environmental conditions, so there is little risk they are unable to handle their design duty. If there is nothing to cause a failure, the failure will not happen and your equipment continues in service at full capacity and full availability.
For maintenance practitioners, it is the use of precision maintenance backed-up with condition monitoring applied as a tool to prove maintenance work is done precisely, that provides the foundation for exceptional equipment reliability.
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Recently, Ziad let me know he published an article titled Analyzing Repairable System Failures Data in the April-May 2017 issue of Uptime magazine (subscription required). He suggested I’d be interested in the article since it provides a way to analyze repairable system data without using MTBF. He was right.
The article is a short description and tutorial on using mean cumulative plotting and function (MCF). While the article recommends staying away from using MTBF, it could be a bit of a stronger message. The article does provide a very nice worked out example illustrating the use of a mean cumulative plot. [Read more…]
Modern militaries don’t win many wars these days. The most dominant, well-funded, highly-trained armies have consistently lost (or at least not won) the Korean War, Vietnam War, Afghanistan War, arguably the Iraq War, and plenty of others. And many dominant, well-funded, highly-trained companies are consistently spitting out unreliable or unimaginative products that smaller and less dogmatic companies have no problem bettering.
To understand why, let’s look at a man called Robert McNamara.
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I had an interesting case study a couple weeks ago, where “I’m giving you what you want, not what you asked for “ when the requirement as usual was a blanket MTBF, but the product design elements clearly indicated wearout could / would be a factor. — Kevin