Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
Article by Mike Freier
Manufacturing is an essential step in a new product launch that requires a thoughtful strategy. In this stage, teams define a manufacturing strategy, create a development schedule, and built units to test.
Dear friends, we are happy to relaese this video on Fault Tree Anlayis FTA). FTA is an important technique used in reliability engineering. Hope you find the video interesting! Link to our video on Binomial distribution
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Article by Mike Freier
In the last blog post, we discussed why Design for Excellence (DfX) is important to your business. Building on several important concepts, this blog will focus on the Product Development phase and discuss how incorporating Agile principles can improve and accelerate your hardware product development process.
[Read more…]Many ideas grow better when they are transplanted into another mind than the one where they sprang up. Oliver Wendell Holmes
In this article, I will outline how to evaluate an FMEA against the FMEA Quality Objective for FMEA Team. I’ll include relevant information from the chapter in the FMEA Preparation series called “Assembling the Correct FMEA Team.”
Dear friends, we are happy to release this video on relatively unknown subject of Extended Reliability Growth Model! Please watch our previous videos on the subject of Reliability Growth before watching this video.
Links are provided below:
Reliability Growth Introduction and Duane Model
Crow AMSAA Reliability Growth Model
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A client wanted to compare the Kaplan-Meier (nonparametric maximum likelihood) estimators of the reliabilities of the old and new products. That is, he wanted me to test reliability functions, Ho: R1(t)=R2(t) for all nonnegative t vs. Ha: R1(t)≠R2(t) for some nonnegative t.
Because I’m lazy and fixed in my ways and because I thought it would be easier to explain, I chose the Kolmgorov-Smirnov (K-S) test [Gnendenko]. It’s convenient, practically every statistics text has the tables, and I can program tables and the test statistic easily. The test uses the maximum absolute difference between the Kaplan-Meier estimates of the two reliability functions. Reject Ho if maximum absolute difference, Dmn=max|R1(t)-R2(t)| exceeds a critical value, where m and n are the two sample sizes.
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Design for Excellence (DfX) is a holistic approach to designing a hardware product that takes into account how the product is made at scale. In addition to understanding your manufacturing plan and target costs, there are six categories to consider: assembly, cost, manufacturing, test, service, and supply chain.
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Dear All, we are happy to release our 76th video on Crow AMSAA Model for Reliability Growth. We recommend viewers to watch the following videos before watching this video for better experience:
(1) Homogeneous Poisson Process (HPP) and Nonhomogeneous Poisson Process (NHPP)
(2) Reliability Growth Concepts and Duane Model with Illustration
We welcome your feedback!
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Mapping good ideas to product development is best done in the concurrent engineering framework, a collaborative approach to new product introduction. Using the MVP and the product roadmap, you are now ready to move into balancing design concepts with business concerns like costs.
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Dear friends, we are happy to release this 75th video of our technical channel ! In this video, Hemant Urdhwareshe explains the concepts of HPP and NHPP for repairable systems. The NHPP is foundation for Reliability Growth! Hemant is a Fellow of ASQ and is certified by ASQ as Six Sigma Master Black Belt (CMBB), Black Belt (CSSBB), Reliability Engineer (CRE), Quality Engineer (CQE) and Quality Manager (CMQ/OE).
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In the next chapter in our series on taking a hardware product from idea to scale, we move into the Design & Planning stage. Minimal Viable Products (MVP) and roadmaps will help you identify the key features for your new product.
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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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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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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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