Your Reliability Engineering Professional Development Site
Maintenance and Reliability practitioners often need to find quick methods to estimate life distributions in order to get some urgent answers to a customer. The tempting solution and easy way out to this is to refer to a handbook or publication out there. Also known as “Reliability Data” handbooks. These publications would have “ready to go” life distributions. However, this can come with multiple pitfalls listed as follows.
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A certain Operations Manager started inventing production KPIs in order to measure reliability from a production perspective. So he got together his colleagues and they came up with this formula.
Reliability = Good Production / (Net Production Hours + Nominal Speed)
When asked to define ‘good production’, I was told that it was the saleable production remaining after losses such as speed losses, first time quality, downtime, change overs, etc. were taken off.
After 3 years of running TPM (Total Productive Maintenance) across 3 factories, they made the following observations. The mean time between failure (MTBF) of equipment in all 3 factories increased. The production volumes increased. However the Reliability remained flat. How can this be? Something is not right. Is the above formula incorrect?
Is there a better way to calculate Reliability from a production perspective?
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This article is the eighth of fourteen parts to our risk management series. The series will be taking a look at the risk management guidelines under the ISO 31000 Standard to help you better understand them and how they relate to your own risk management activities. In doing so, we’ll be walking through the core aspects of the Standard and giving you practical guidance on how to implement it.
In previous articles we’ve looked at the core elements of the risk management framework, as well as the role of leadership and commitment, integration, design, implementation, evaluation and improvement more specifically. In this article, we’ll be moving away from the framework and instead introducing you to the risk management process.
Maybe. Great care must be taken if any kind of template of failure mode library is used to complete an RCM analysis.
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Below is summary (annualized average) of 20-year pipeline incident data from 1990-2009. [Source: Pipeline and Hazardous Material Safety Administration,PHMSA]
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How much should you pay attention to readability scores generated from tools like the Flesch-Kincaid Readability Formula? As a technical professional, you probably should pay careful attention. But remember, while improving the Flesch-Kincaid score is important for accessibility and readability, balancing this with accurately conveying the technical information is essential.
Readability is a quality of your business writing. People will be able to understand your sentences easily if your text’s readability is high. If the readability is low, people still might understand what you’re saying, but reading your text is likely a draining experience, but people may still understand it.
Big words and complex sentences aren’t bad. Using too many of them demands much more concentration from your reader. Big words and complex sentences are also harder if someone’s first language is not yours or the reader has some form of visual or hearing impairment.
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Barringer Process Reliability (BPR) was developed by Paul H. Barringer, a fellow reliability engineer “extraordinaire” and an outstanding mentor for myself and countless others in this field of practice. BPR highlights operational issues. Not addressed and mitigated, those could have significant revenue impacts. A BPR analysis uses the Weibull probability plot which happens to be a very well-known tool in the field of Reliability Engineering. On one side of a sheet of paper only, the BPR plot can tell the true “story” on the operation.
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“Too many cooks spoil the broth” goes the Elizabethan poet George Gascoigne’s proverb. Although only written down in circa 1575 it had probably been around for many years beforehand. It is still used today and, far from being archaic, it’s become more and more relevant despite mankind’s predilection towards efficiency and effectiveness. But why?
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James Reyes-Picknell
Despite its well-documented successes, Reliability Centered Maintenance (RCM) has always drawn a lot of discussion and controversy. Much of it is because of a lack of understanding and “myths” generated to discredit RCM as a viable business solution. Here we attempt to fill in some of those gaps in understanding and debunk some of the myths.
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To help select which work orders to do first in situations of resource shortage many CMMS provide calculations for maintenance work order priority. Deciding maintenance work priority is a risk decision. The presence of risk totally changes the way to allocate maintenance job priority if you want to compare situations equally1. When you work with risk you cannot use a linear priority scale. Using linear priority ranking gives the wrong order of importance for doing maintenance work.
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EPA announced that the promulgation of National Emission Standards for Hazardous Air Pollutants (NESHAP) for industrial, commercial, and institutional boilers and process heaters is postponed to January 16, 2011. The regulation, commonly referred to as Boiler MACT, will affect approx. 13,500 boilers at various facilities deemed to be major sources of hazardous air pollutants (HAPs).
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In a previous article we covered how to perform a detailed Weibull Analysis in Excel. The outputs from a Weibull analysis are important because we can use them for a variety of Reliability calculations such as when to most economically maintain assets.
A common mistake we see made is Reliability Engineers determining the optimal maintenance interval as the MTBF. This is incorrect as it assumes that you will have failed approximately 50-60% of the components before you have maintenance performed on them. (Sounds ridiculous when you say it that way right!?)
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The current state of the quality profession is affected by shifting business infrastructures and changing definitions of brand quality.
Businesses need to react and change against external pressures like increased frequency of consumer communications, the availability of big data, expanding regulations and standards, and the expectations to innovate quickly. The quality profession is at risk of losing its effectiveness in the overall business operations if it does not proactively change with the business.
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A tutorial explaining the Physics of Failure method applied to regularly failing roller bearings in a dewatering press. After three years of exhaustive efforts to solve the cause of the bearing failures it was decided to test Physics of Failure Analysis with the aim of finding a lasting answer.
In the world of industrial and manufacturing enterprises, where precision and reliability are paramount, maintenance leadership stands as the unsung hero. Behind the scenes, these individuals and teams are the custodians of high-quality operations, ensuring that machinery hums with efficiency, downtime is minimized, and standards are not just met but exceeded. Let’s take a closer look at the essence of exemplary maintenance leadership and why it’s the cornerstone of a well-oiled operation.
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