Your Reliability Engineering Professional Development Site
AFRs are periodic ratios of failure counts divided by installed base. Have you seen meeting rooms wallpapered with AFR charts (Annualized Failure Rate)? Have you sat through debates about the wiggles in AFR charts? Fred Schenkelberg wondered if reliability could be estimated from AFRs and their input data? How about age-specific reliability and actuarial failure rate functions? Actuarial forecasts? MTBFs? Wonder no more!
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By Jesús R. Sifonte
Condition Monitoring is a broad term referring to the systematic process of data collection for the evaluation of asset’s performance, reliability and maintenance needs with the purpose of planning repair works. Its main purpose is Potential Failures finding. It requires the collection of good asset’s health data which trending is studied. The primary advantage of Condition Monitoring is that it incorporates health indicator monitoring activities performed while the machine is operating. Assets failures are predicted well in advance of their occurrence. It allows for planning repairs safely and economically for the plant. Also, machine parameter data trending allows extending assets operation as close as possible to their actual useful life. Condition Monitoring data provides vital information for taking important decisions affecting plant operation goals. Maintenance decisions are taken based on the actual asset condition avoiding unnecessary repairs leading to start up failures. Catastrophic failures of a critical assets presenting accelerated wear trends can be avoided by using C tasks too. Sometimes operating conditions changes causing components life expectance to reduce as noted by steeper indicators trends leading to unexpected catastrophic failures. This can be detected by CM and earlier planned shutdowns can avoid such disasters.
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Suppliers and users of any product want that it performs well during its lifetime. That is, the item must perform within specified operating parameters during its life cycle. The life cycle of an item comprises Concept, Research & Development, Production, Operation & Maintenance and, Disposal phases. Each phase carry costs its owner wishes to minimize. The idea is to realize the most value from the item when the whole life cycle costs and benefits are considered. In most cases, usually 80% of the total costs are incurred during the operation & maintenance phase of the life cycle. Machine failures cause plants to stop production causing accidents, economic impacts and reputation loses. Asset components gradual degradation with age, operational/maintenance errors and design flaws all can cause assets or processes to fail. A failed asset is considered unreliable, which means that it is no longer able to fulfill its intended function.
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Let’s think of this as a crowdsourced project. The first version of this book is a compilation of NoMTBF.com articles. It lays out why we do not want to use MTBF and what to do instead (to some extent).
With your input on success stories, how to make progress using better metrics, and input of examples, stories, case studies, etc., the next version of the book will be much better and much more practical.
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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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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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Chris and Fred discuss the Military Handbook 217 Version F (from 1991 no less) … again? Ever heard of it?
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Destiny is no matter of chance. It is a matter of choice. It is not a thing to be waited for, it is a thing to be achieved.” – William Jennings Bryan
The Oxford English dictionary defines “reliability” as “the quality of being able to be trusted to do what somebody wants or needs.”
The textbook definition for “reliability” is “the probability that an item will perform its intended function for a designated period of time without failure under specified operating and environmental conditions.”
In this article, I will share a brief outline of the current and future state of reliability engineering, what works and doesn’t work, and why it matters to all of us.
Azmat Siddiqi suggested a certification in reliability statistics in 2022. Azmat believes in knowing and using the reliability statistical information in test, installed base, failures, and service data. Thanks Azmat.
I propose Certification in Reliability Statistics to recognize statistics knowledge, work experience, and applications. Certification in Reliability Statistics should provide assurance to employers, contractors, and collaborators that reliability statistics are estimated and used to the best extent with available data, including uncertainty quantification, with or without life data.
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The LinkedIn ASQ RRD group published this question from a reliability manager. Replies included:
Proponents of the Continuous Improvement method often quote the dictum ‘what gets Measured, gets Improved’. I’d like to modify it by adding ‘what gets Monitored…’ to its beginning. Here I’m referring to the Monitoring of the physical assets in their usage conditions and being Measured & Improved for their Reliability (Availability %, Cost $, MTBF, etc.) and Safety metrics.
[Read more…]Chris and Fred discuss the naked mole rat … an animal that doesn’t appear to age at all! Ever! And they are ugly …
Want to know why we are talking about these animals and what that has to do reliability? Listen to this podcast!
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Chris and Fred discuss one of the challenges of allocating reliability … in response to a question from one of our listeners. Might you be able to learn something as well!
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My wife says I am wasting my time trying to change reliability statistics, so I polled the www.linkedin.com Reliability Leadership…, ASQRRD, IEEE Reliability, “Biostatistics, and No MTBF groups. The polls claimed that “Life data, censored or not, is required to estimate MTBF, reliability function, failure rate function, or survivor function. TRUE? FALSE? or DON’T KNOW.” I am grateful for the responses.
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