I estimated actuarial failure rates, made actuarial forecasts, and recommended stock levels for automotive aftermarket stores. I wondered how to account for seasonality in their sales? Time series forecasts account for seasonality but not for age, the force of mortality accounted for by actuarial forecasts. I finally figured out how to seasonally adjust actuarial forecasts. It’s the same method, David Cox’ “Proportional Hazards” model, used to make “Semi-Parametric” estimates and “Credible Reliability Predictions”.
[Read more…]Search Results for: mtbf
Shaping Organizational Behavior
When conducting a Human Reliability Assessment (HRA), we use the terminology errors of commission or errors of omission. It behooves every professional to question why we focus on one metric in preference to all others in an objective and constructive manner in order to discern whether we are exposing our organization to errors of professional omission or commission. The other conclusion is that we are doing the right thing and this is also an empowering piece of knowledge.
Beware of the Mean Time Between Failure Calculation Trap
An MTBF calculation is often done to generate an indicator of plant and equipment reliability. An MTBF value is the average time between failures. There are serious dangers with the use of MTBF that need to be addressed when you do an MTBF calculation.
Take a look at the diagram below representing a period in the life of an imaginary production line. What is the MTBF formula to use for the period of interest to represent the production line’s reliability over that time? [Read more…]
Do the Best You Can With Available Data?
Lifetime data is nice to have, but lifetime data is not necessary! Generally Accepted Accounting Principles require statistically sufficient data to estimate nonparametric reliability and failure rate functions. Some work is required!
ISO 14224 “Petroleum, Petrochemical and Natural Gas Industries—Collection and Exchange of Reliability and Maintenance Data for Equipment” requires lifetime data to estimate exponential or Weibull reliability functions! Sales or ships and returns or failure counts are statistically sufficient to make nonparametric estimates of reliability and failure rate functions, without unwarranted distribution assumptions or lifetime data!
[Read more…]Use the Right Fit
I’ve often railed on and on about the inappropriate use of MTBF over Reliability. The often cited rationale is, “it is simpler”. And, I agree, making simplifications is often necessary for any engineering analysis.
It goes too far when there isn’t any reason to knowingly simply when the results are misleading, inaccurate or simply wrong. The cost of making a poor decision based on faulty analysis is inexcusable.
Why Use Nonparametric Reliability Statistics?
Fred asked me to explain why use nonparametric statistics? The answer is reality. Reality trumps opinion, mathematical convenience, and tradition. Reality is more interesting, but quantifying reality takes work, especially if you track lifetimes. Using field reliability reality provides credibility and could reduce uncertainty due to tradition and unwarranted, unverified assumptions.
Data is inherently nonparametric. Cardinal numbers are used for period counts: cohorts, cases, failures, etc. Accounting data is numerical; it is derived from data or from dollars required by GAAP (Generally Accepted Accounting Principles); e.g., revenue = price*(products sold), service cost = (Cost per service)*(Number of services), or numbers of spare parts sold. Why not do nonparametric reliability estimation, with or without lifetime data?
[Read more…]The Language We Use Matters
During RAMS this year, Wayne Nelson made the point that language matters. One specific example was the substitution of ‘convincing’ for ‘statistically significant’ in an effort to clearly convey the ability of a test result to sway the reader. For example ‘the test data clearly demonstrates…’
As reliability professionals let’s say what we mean in a clear and unambiguous manner.
Thus, you may suspect, this topic is related to MTBF.
[Read more…]Do Not Want Equipment Failures
I am a rock climber. Climbing relies on skill, strength, knowledge, luck, and sound gear. Falling is a part of the sport, and with the right gear, the sport is safe. So far, I’ve enjoy no equipment failures.
I do not know, nor want to know, the MTBF (or MTTF) of any of my climbing gear. I’m not even sure this information would be available. And, all the gear I use has a finite chance of failing every time the equipment is in use. Part of my confidence is that the probability of failure is really low.
[Read more…]SOR 940 When I Know it’s Not Right
When I Know it’s Not Right
Abstract
Carl and Fred discuss a question brought up at recent conference: what do you do when you are supposed to do something that you know is not the right thing to do? The context was reliability engineering and management.
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First Impressions
Note: This first article in the NoMTBF campaign was published on April 1st, 2009. Thus, we’ve been at this and making progress for a long time and come a long was since starting the NoMTBF campaign. I am looking forward to your comments, contributions, and suggestions.
Fred
At first, MTBF seems like a commonly used and valuable measure of reliability. Trained as a statistician and understanding the use of the expected value that MTBF represented, I thought, ‘Cool, this is useful.’
Then, the discussions with engineers, technical sales folks, and other professionals about reliability using MTBF started. And the awareness that not everyone, and at times it seems very few, truly understood MTBF and how to properly use the measure.
[Read more…]Free Safety And Reliability Resources
A listing of online tools and resources that may interest safety or reliability professionals. Always check how well the tools work before using for serious decisions.
[Read more…]Convert AFRs to Field Reliability?
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!
[Read more…]A Systematic Process of Data Collection
Article first posted at Conscious Reliability by James Reyes-Picknell, Jesus Sifonte, and team.
Condition Monitoring – A Closer Look
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.
[Read more…]Reliability Engineering Applied to Maintenance (REAM)
Article first posted at Conscious Reliability by James Reyes-Picknell, Jesus Sifonte, and team.
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.
[Read more…]Getting Life Distributions from “Reliability” Handbooks? Be Careful!
The need for life distributions
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.
[Read more…]