Your Reliability Engineering Professional Development Site
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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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
Creating a product or system that lasts as long as expected, or longer, is a challenge.
It’s a common challenge that reliability engineering and the entire engineering team face on a regular basis. It’s also not our only challenge.
We face and solve a myriad of technical, political, and engineering challenges. Some of our challenges are born and carried forward by our own industry. We have tools suitable for a given purpose altered to ‘fit’ another situation (inappropriately and creating misleading results). We have terms that we, and our peers, struggle to understand.
Sometimes, we, as reliability engineers, have set up challenges that thwart our best efforts to make progress.
Let’s examine a few of the self made challenges and discuss ways to overcome these obstacles permitting us to tackle the real hurdles in our path. [Read more…]
Imagine product or part lifetime as the service time in a self-service system. Suppose you could observe periodic input and output counts of that self-service system, without identifying individual service times. How would you estimate the service time distribution without following individual products or parts from input to output? The maximum likelihood reliability estimator for an M/G/Infinity self-service-time distribution function from “ships” (inputs) and “returns” (outputs) counts also works for nonstationary arrival process M(t)/G/Infinity self-service systems, under a condition! A constant or increasing arrival (inputs or ships) rate satisfies the condition. If you identify returns by failure mode then you could estimate make nonparametric estimates of field reliability by failure mode and quantify reliability growth, without life data, if the condition is satisfied.
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Is there any useful result from a parts count prediction?
In most cases that I’ve seen parts count predictions used they are absolutely worthless. Worse, is the folks receiving the results believe they are accurate estimates of reliability performance (or at least use the results as such).
In my opinion, the range of parts count prediction methods and databases harm the field of reliability engineering.
We need to call out the poor results, promote better practices, and stop the vapid use of such a poorly understood tool. [Read more…]
I learned actuarial methods for forecasting and spares planning while working for the US Air Force Logistics Command in the 1970s [AFLCM 66-17 and AFM 400-1]. I am grateful for the education, and I am sorry to report that the USAF has reverted to MTBF management.
The US AFLC actuarial methods were developed for engine management in the 1960s by RAND Corp. [Giesler] They estimated age-interval failure rates and made actuarial forecasts of engine demands depending on the flying-hour program plan. An actuarial forecast is ∑a(s)n(t-s), s=1,2,…,t, where a(s) is actuarial failure rate conditional on survival to age s and n(t-s) is the installed base of age t-s. Periodic meetings consolidated engine lifetime and failure data into agreements on actuarial failure rates, for forecasting engine demands and for war readiness spares requirements.
The USAF actuarial methods assume constant failure rates within short age intervals, Poisson demands, and ignore variance induced by variable flying hours per aircraft in the flying hour program. I later figured out how to estimate actuarial failure rates for all engines, major modules, and their service parts, with or without life-limits and without lifetime data; I computed the distribution of demand forecasts, not Poisson. I offered to show AFIT faculty, AFOSR, AFRL, and RAND how to extend actuarial methods to all service parts [George, 1993].
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Carl and Chris discuss the changing opportunities and challenges with AI and reliability engineering. What are the positive interactions, and where should we be concerned?
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Everyone knows what reliability is. And that’s the problem. We have used the term ‘reliability’ in normal, everyday speech since we were children. So that means we have decades of preconceived ideas about what reliability is (and is not). But that means there are lots of different ideas about what reliability is. Some people think this thing called the ‘MTBF’ is a measure of reliability (it isn’t). And neither is the ‘failure rate.’ Neither are things called the ‘L10’ and ‘B10’ lives. So if you want to find out what reliability actually is (no equations) … this webinar is for you!
[Read more…]
Jobs at Apple has done it. You can, too.
Change an industry. The advent of iTunes and iPods forever changed how the world buys and listens to music.
While Jobs had the resources of Apple to help make the change happen. It still started as an idea (may or may not have been Jobs’ idea, I don’t know). It grew and created enough momentum to effect a change across an entire industry.
Change is hard.
If you have tried to help your team move in a new direction or consider the reliability risks present in the current design, then you know change is difficult to make happen. You most likely have been successful a few times, and not a few also. I know I’ve crashed into the rocky spit more often than I can count. [Read more…]
In this episode of Reliability — It Matters, we’re heading to space — and straight into the fascinating world of probability plots, random failures, and what your data really says about your assets.
Our guest, an engineer from Blue Origin, joins us to unpack the crucial difference between looking at failure data for a single component versus an entire fleet. We dig into why probability plots are powerful tools for spotting patterns, busting myths like the misuse of MTBF, and how good (or bad!) data practices can make or break your reliability insights — whether you’re launching rockets or keeping things running here on Earth.
If you’ve ever wondered how to get real insights from failure data — beyond the averages and assumptions — this one’s for you. Buckle up! 🚀🔍
You can reach out to Ayaz through Linkedin.
Everyone knows what reliability is. And that’s the problem. We have used the term ‘reliability’ in normal, everyday speech since we were children. So that means we have decades of preconceived ideas about what reliability is (and is not). But that means there are lots of different ideas about what reliability is. Some people think this thing called the ‘MTBF’ is a measure of reliability (it isn’t). And neither is the ‘failure rate.’ Neither are things called the ‘L10’ and ‘B10’ lives. So if you want to find out what reliability actually is (no equations) … this webinar is for you!
[Read more…]
Article by Mike Freier
In the last blog post, we discussed how incorporating Agile principles can improve your hardware product development process. Not only will you be able to accelerate your schedule, but you can also develop higher quality products with lower costs and reduced risks. Furthermore, Agile frameworks and techniques can fit into your existing new product introduction (NPI) and stage gate process, so your high-level standard operating procedures do not have to change.
Once you have engineering and beta units in hand, validation and verification can begin. In this stage, engineering teams should test your product against the engineering design specification to verify that your product meets the performance, reliability, regulatory, manufacturing test, and safety goals.
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MTBF, KPIs, yield, return rate, warranty… bah!
We may use one or more of these when establishing product reliability goals. When tracking performance. When making decisions.
Goals, objectives, specifications, and requirements, are stand-ins for the customer’s experience with the product.
We’re not trying to reduce warranty expenses or shouldn’t be solely focused on just that measure. We need to focus on making decisions that allow our product deliver the expected reliability performance to the customer. [Read more…]