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Find all articles across all article series listed in reverse chronological order.
The Weibull distribution’s popularity across various industries stems from its remarkable flexibility and adaptability in modeling a wide range of data types, especially in reliability engineering and failure time analysis. This versatility is primarily due to the Weibull distribution’s shape parameter, which allows it to model different failure rates over time, aligning closely with the real-world behavior of many systems and components.
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By it’s cover no doubt. The title and cover are important, this is true. When you judge a reliability book we often first see and evaluate the cover.
The author? Do you buy the book based on who wrote or edited it?
Do you have a quick scan or check for key features before you add the book to your library? I’m curious how you select a book to use a reference for your work. The books we read and use for work shape our work, thus it’s important to have the right works at our disposal. [Read more…]
WHEN YOU DO A RELIABILITY FAILURE STUDY LOOKING TO PROTECT YOUR BUSINESS AGAINST LARGE UNCOMMON FAILURES, AND THOSE FAILURES THAT ARE VERY COMMON BUT THOUGHT TO BE TOO SMALL TO BE A SERIOUS PROBLEM, BE SURE TO DO LIFETIME FINANCIAL ANALYSIS WHEN DECIDING THE FAILURES TO INVESTIGATE
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[Read more…]I am to do a reliability failure study and would like to know the definition of repetitive failure with respect to some recognized reliability standard.
We want to apply the recommendations in the standard for failure codes in our CMMS system to decide which equipment failures need a reliability failure study. For example, we have a centrifugal pump bearing fail twice in a year. I am of the opinion this is a repetitive failure.
Over the next 10 years, I expect many more industries to be disrupted by software, with new world beating Silicon Valley companies doing the disruption in more cases than not.
Marc Andreessen – Venture Capitalist
We’ve been involved in engineering and tech automation for years. What surprises us is the observable breadth, depth, and impacts of automation (AI included) over the last five years, which COVID accelerated. What’s the big deal about automation?
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This article appeared in 1983 in the ORSA Applied Probability SIG under the pseudonym “Anonymous”
In another country far away, there were power plants that generated cheap electricity by unclear means. Since operation of the plants involved some risk, the Unclear Regulatory Commission was established to license power plants for operations. The URC decreed that any plant could have a license if its probability of an unclear accident was smaller than 0.000001 per year. The plant operators said they were uncertain.
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In today’s short video, I share how the delivery of our new metal building reminded me of a key aspect of Reliability Centered Maintenance (RCM). RCM was designed to be carried out with a Facilitated Working Group approach, bringing together a team of equipment experts to answer the RCM questions.
By using this approach, not only do you get comprehensive answers, but you also capture and memorialize the knowledge and experience of your experts—something that could amount to over 100 cumulative years’ experience in the room!
Even if one of your team members retires or moves on, their knowledge remains. Watch to find out how you can make the most of this valuable resource.
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Dear friends, we are happy to upload this video on how to estimate B10 life when failure data follows Weibull or Lognormal Distribution. Your feedback on the video is welcome!
This video from the Institute of Quality and Reliability explains how to determine BX life for products that follow Weibull and log-normal distributions. BX life is defined as the time by which X percent of items are expected to fail. For example, B10 life means that 10% of items are expected to fail, which implies a 90% reliability.
We recommend watching following videos before watching this video for better learning experience:
B10 Life for Exponential Distribution
Normal Distribution and Z-Score
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The feasibility phase addresses two questions. First, can your design be made into a manufacturable product? And, second, how will you sell your product and to whom?
Validating the feasibility of manufacturing a new product is the next chapter in our series by Mike Freier on how to take a new hardware product from idea to scale.
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Continuous Improvement in product development is a systematic and ongoing approach to enhancing products processes and services. This concept is crucial for organizations to stay competitive, meet evolving customer needs, and maintain product quality.
Let’s explore the key concepts of continuous Improvement in product development. Continuous Improvement is an iterative process tha 2involves constantly evaluating and refining products and processes. It’s not a one-time effort but a cyclical approach that allows for ongoing enhancements based on feedback.
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“Do not improve reliability as it cuts into our repair activity profits.” Is this a way to run a reliability program?
I’ve seen this in action and that company is no longer in business. In another situation the field service department withheld vital information to improve products lest his department (and self-importance) dwindle.
Is this a bad business model, or is it just my thinking it not so smart? [Read more…]
So, when should a company self-disrupt? Or, more importantly when should you self-disrupt.
Companies face these disruptor questions:
Plant and equipment do not fail by accident. There are causes. Whether equipment fails depends on the capacity of their parts to handle stresses, when stresses are applied and the size of those stresses. These are probabilistic events – they are random, with many possibilities. Timing the start of failure, or its continuation to a breakdown, is mostly speculation because it depends on which past and future scenarios occur. It seems that luck and chance has a large say on the lifetime reliability of equipment. But there is a way to guide equipment reliability and performance toward the results you want.
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SAS, JMP, R-”Survival”, Minitab, ReliaSoft, XLStat, and perhaps other statistics programs offer the Kaplan-Meier nonparametric reliability estimator as a default. Take credit for using nonparametric reliability estimation and avoiding unwarranted assumptions. What could go wrong using the Kaplan-Meier estimator?
Are you using all the information in data available from population data required by GAAP? If you don’t have lifetime data, use periodic failure counts. This article describes an example where the Kaplan-Meier estimator from grouped lifetime data is less efficient than using periodic failure counts, even though you don’t know which cohort they came from!
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Today’s Gremlin – Cheap Charlie, is a well intentioned but often mis-guided Gremlin. He or she can be many people and we’ve all dealt with them before. “Cheap Charlie” is someone who truly believes that not spending leads to profits. This is one of the many gremlins we talked about when we introduced this series of articles.
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This video from the Institute of Quality and Reliability explains how to calculate the B10 life (the time by which 10% of items are expected to fail) for an exponential distribution.
We suggest to view our video on Exponential Distribution for better learning experience. Here is the link to the video: Remember this Memoryless Exponential Distribution . Your feedback is welcome!
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