Your Reliability Engineering Professional Development Site
Find all articles across all article series listed in reverse chronological order.
Dear friends, we are happy to released this sixth video in our series on hypothesis testing! In tis video Hemant Urdhwareshe has explained procedure to perform chi-square test in case of contingency tables. Our objective is to compare different groups to assess whether these are independent or related.
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Welcome back to the channel! I’m Nancy Regan, and today I’m bringing you a special tutorial from Key Largo, Florida. Join me as we dig into Condition Based Maintenance (CBM) and uncover the vital concept of the P-F Interval right here in the sand.
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The four-part series of communication tips on “How to Ask Better Questions.” The primary audience is technical professionals who serve as facilitators of team collaboration. However, the approaches and techniques apply to just about anyone. This series summary of helpful tips and insight, including a brief description and a link to each article.
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Root Cause Analysis (RCA) is a systematic process for identifying the underlying causes of problems and preventing their recurrence. An effective RCA not only resolves the current issue but also provides insights for improving processes and systems. This comprehensive guide explores the detailed steps involved in performing an expert-level RCA.
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Degradation testing for electromechanical components, such as pumps, valves, and sensors, involves a series of steps to identify wear and tear that could lead to system failure. The goal is to detect these signs of degradation early enough to replace the component before it causes a failure.
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MTBF is a symptom of a bigger problem. It is possibly a lack of interest in reliability. Which I doubt is the case. Or it is a bit of fear of reliability.
Many shy away from the statistics involved. Some simply do not want to know the currently unknown. It could be the fear of potential bad news that the design isn’t reliable enough. Some do not care to know about problems that will require solving.
Whatever the source of the uneasiness, you may know one or more coworkers who would rather not deal with reliability in any direct manner.
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Two schedule tools are widely used for the Integrated Master Schedule (IMS )around the world to manage projects. The first one is Microsoft Project (MS Project). The second one is Primavera P6. They are both very good. Primavera P6 is an Oracle product originally designed for the construction industry. MS Project is simpler and easier to learn based on my experience. It also costs less than Primavera P6. DOD uses MS Project and typically requires it for use on government contracts. Other industries use one or the other depending on the size and complexity of the project.
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Industrial operations that use physical assets for production or service delivery need their assets to run optimally, and cause minimal operational disruptions when they don’t. Spending too little overall, or on doing the wrong things, always backfires. These short-term savings inevitably lead to breakdowns disrupting operations that cost much more in lost revenue than was saved by under-spending.
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To reduce maintenance costs and production downtime it is necessary to reduce the causes of the maintenance and downtime. Both maintenance and downtime are an effect and not a cause. The causes can be traced back to defects and errors from a variety of sources. Knowing that defects eventually lead to future equipment failures, production downtime and lost profits, it is necessary put strategies into place to purposely prevent them occurring in the first place and to eliminate them if they are present.
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In Part 1 of this article series, we described validation engineering as the process of validating, monitoring and execution of product requirements to ensure customer needs are met. The validation engineer is the validation process owner and works with subject matter experts to develop complete, accurate and testable requirements.
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“It is the policy of my Administration to respond to the coronavirus disease 2019 (COVID-19) pandemic through effective approaches guided by the best available science and data” [Biden Executive order, 2021]. That epidemic inspired the simultaneous nonparametric estimation of survival functions from case to recovery and case to death, without lifetime data (figure 1)!
Why not do the same for multiple-failure-mode data? This article shows nonparametric, multiple-failure-mode, maximum likelihood reliability estimation in a spreadsheet. Data are system first-failure times and the corresponding failure modes that caused the first system failures (table 1). However those data are dependent. I will explain the likelihood function, lnL, and how to find the maximum likelihood reliability estimates for all failure modes simultaneously.
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Dear friends, we are happy to released this fifth video in our series on Hypothesis Testing! In this video, Hemant Urdhwareshe explains the applicability of Chi-square test of one variance with an illustration! Hemant is Fellow of ASQ and is one of the few Certified Six Sigma Master Black Belts from ASQ having six certifications from ASQ.
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Supervisors in maintenance play a crucial role in ensuring the efficient operation of equipment and facilities. To be effective in this role, they need a combination of technical knowledge and management skills. Some key management skills necessary for a supervisor in maintenance include:
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I’m Nancy Regan, joining you from the beautiful Key Largo, Florida. Today, we’re delving into a significant pitfall in Reliability Centered Maintenance (RCM) — not fully understanding the process. Just like a boat captain needs to know the waters, successful RCM implementation requires a deep understanding of its principles.
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The term Measurement Systems Analysis refers to a collection of experimental and statistical methods designed to evaluate the error introduced by a measurement system and the resulting usefulness of that system for a particular application.
Measurement systems range from the simplest of gages like steel rulers to the most complex, multi-sensor measurement systems. Yet regardless of their sophistication, all gages are flawed and fail to deliver a perfectly accurate result to their users. This idea is best expressed by an equation fundamental to measurement science,
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