An Idea Short Explainer Videos
Abstract
Chris and Fred discuss how short, 1-minute explainer videos could help reliability engineers … especially new ones!
ᐅ Play Episode
SOR 972 An Idea Short Explainer Videos
Search Results for: MTBF
Join the Linkedin Group
The Linkedin NoMTBF group is growing and while not very active does have an occasional interesting discussion. Join the discussion and maybe relate how you have raised awareness around the proper use of MTBF.
https://www.linkedin.com/groups/1857182/
[Read more…]Arrhenius or Erroneous
The following is a recent discussion on the sister Linkedin NoMTBF Group. It was and may continue to be a great discussion. Please take a look and comment on where you stand. Do you have some form of the Arrhenius reaction rate equation in your reliability engineering work? Join the discussion here with a comment or on the Linkedin group conversion.
Fred
[Read more…]Lesson 2 – Parametric Models
Module 11 Repairable Systems Analysis
Lesson M11-02
Duration: 25 minutes
As in the non-repairable case, we can fit parametric models to the time between failure data. Two categories of parametric models are presented: the Homogeneous Poisson Process (HPP) and the Non-Homogeneous Poisson Process (NHPP). The HPP implies that the failure rate (ROCOF) is constant over time. The usefulness of the Mean Time Between Failure statistic (MTBF) depends on the type of parametric model used. An HPP model fit is demonstrated using Minitab software since Reliasoft only uses a more general approach. The Power Law (or Power ROCOF) model is introduced, and this model handles non-constant failure rates. An example of a Power Law model is shown using Reliasoft.
Reliability Reference Textbook – Section 11, Pages 10-22
Semi-Nonparametric Reliability Estimation and Seasonal Forecasts
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…]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.
ᐅ Play Episode
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…]