Your Reliability Engineering Professional Development Site
Probably every engineer has heard once in his experience terms FMEA and RCA.
What is FMEA?
FMEA is a systematic method for identifying and preventing potential failure modes in a product or process. It helps in proactively addressing failure risks, improving reliability, and enhancing safety. FMEA is crucial in various industries, including automotive, aerospace, and manufacturing, to ensure product quality and customer satisfaction.
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Once upon a time in my former life, I served as the Quality Manager for a $300M tier-one automotive manufacturing plant that provided components and assemblies to OEM vehicle assembly plants. You would recognize our customers – most of the big names. This assignment was a departure from my previous roles in product development and testing. To say that the job was eye-opening is an understatement. Years later, I described this three-year experience to friends as my “tour of duty on the front lines.” Anyone who has spent any time in manufacturing can certainly relate!
Every physical asset has a lifecycle starting at conception through to its eventual disposal and site remediation. In each phase of the lifecycle errors can occur that cause defects within the asset. In operations with hundreds of assets there easily can be tens-of-thousands of defects. Each defect is a risk that eventually contributes to breakdowns, losses, wastes, safety incidents during operation, and shortens the asset’s service lifetime. Eliminating thousands of defects and risks forever is what Plant Wellness Way EAM does for its users
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We should all be aware by now that business agility is the primary enabler for companies seeking sustainability. In the past, companies would evolve in chunks, a project at a time. The costs and risks of implementing change, whether system-related or otherwise, were large, so designing and planning upfront (the Waterfall approach) was applied to create predictability and moderate risks.
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The Kaplan-Meier reliability estimator is the nonparametric, maximum likelihood estimator from right-censored, grouped lifetime data. It has been used since publication, most statistics programs do it, and it has been taught since I was in school. I give away a spreadsheet version.
Lifetime data requires tracking individual subjects or units from their start to failure, death, or censoring. Data may be collected periodically grouped by cohorts: monthly sales, ships, or other collections of individuals, subjects, or units and each cohort’s lifetimes. Data could be displayed in a “Nevada” table with random cohorts in one column, and each cohort’s lifetimes grouped in periodic age-at-failure intervals in columns to the right [Schenkelberg].
A longer video from Nancy with an introduction to the reliability centered maintenance, proactive maintenance, plus the anatomy of a well-planned RCM program.
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Dear friends, some of the participants in our training programs requested me to make a video on how to use Excel to plot histograms and descriptive statistics using Analysis ToolPak. This video illustrates how to do this for a sample data of admit time of patients in a hospital. Hope you find this useful.
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Optimum replacement time – Theory
The optimal asset replacement time calculation has one goal, and that is to calculate the lifetime of when assets should be operational as to have the lowest possible operating cost over its lifetime.
This means balancing two opposing forces, namely the increasing operational cost and unreliability of the asset as it ages, with the decreasing effective cost of ownership. [Read more…]
I am currently reading the book Survival Analysis: Techniques for Censored and Truncated Data, Second Edition (John P. Klein and Melvin L. Moescheberger). Although the techniques presented in this book focus on applications in biology and medicine, the same statistical tools can also be applied to disciplines ranging from engineering to economics and demography. I have a background in mechanical engineering and am interested in applying survival modeling concepts to data from reliability engineering, manufacturing and quality assurance. This article is the first of, hopefully, many articles that I intend to write as I finish reading different chapters from the book.
The data set(s) that will be analysed are the ones that have been used as examples in another book: Statistical Methods for Reliability Data, Second Edition (William Q. Meeker, Luis A. Escobar, Francis G. Pascual). Both the books I mentioned are excellent resources for anyone who is interested in learning more about this topic.
In this article, we will analyze vehicle shock absorber failure time data Failure time data is also known as survival data, life data, event-time data or reliability data, depending on the field of study. and estimate a few basic survival quantities. The data contains failure times (in kilometers driven) and the mode of failure, first reported by O’Connor (1985) O’Connor, P. D. T. (1985). Practical Reliability Engineering. Wiley. [54, 610]. We will ignore the mode of failure for now and will only consider whether a failure occurred or not, i.e., censored. In a future article, I plan to use the different failure modes to discuss competing risks for time-to-failure data.
Well, this article will be a little bit challenging and full of statistical terms. I would categorize the “Hypothesis testing” as most of common use in statistical analysis.
First time of usage happened in 1700s, but actual popularisation at early 20th century:
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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…]
Discover 15 ways to operate so there is less maintenance, fewer maintenance people, lower maintenance costs and (because there is less maintenance) your plant runs reliably for longer! Most of the 15 topics covered are new to operations and maintenance. Each one reduces downtime, save money and improves operating uptime and performance.
We have many kinds of assurance in the project management world. These, generically include business assurance to track the economic viability of a project, technical assurance for the technical integrity of a product, process or system, and user assurance to check that specified requirements have been met. Assurance demonstrates compliance to a project’s business case, meeting end user’s operational requirements, and that there is technical compliance against codes, standards or specifications.
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George Williams, Founder and CEO of ReliabilityX, discusses how Weekly Scheduling helps drive improvement in the efficiency of the maintenance organization! Have any questions? Drop a comment below!
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