Solving Type III problems
There are occasions when we perfectly solve the wrong problem. This is a Type III error.
Following the statistics idea of Type I and Type II errors, when a sample provides information incorrectly about a population, Type III is the error of asking the wrong question to start.
Solving the wrong problem, even perfectly, is still an error.
So, how do you know you’re in a Type III situation?
Continue reading Solving Type III problems
Who are you fooling with MTBF Predictions?
All models are wrong, some are useful. ~ George E. P. Box
If you know me, you know I do not like MTBF. Trying to predict MTBF, which I consider a worthless metric, is folly.
So, why the article on predicting MTBF?
Predicting MTBF or creating an estimate is often requested by your customer or organization. You are being specifically asked for MTBF for a new product.
You have to come up with something.
Continue reading Reliabilty Predictions
In the last note, we calculated MTBF using some test data. Now let’s start with the same situation and calculate reliability instead. As before: There are occasions when we have either field or test data that includes the duration of operation and whether or not the unit failed.
Continue reading Calculating reliability from data
Finding and eliminating early life failures
MTBF for electronics life entitlement measurements is a meaningless term. It says nothing about the distribution of failures or the cause of failures and is only valid for a constant failure rate, which almost never occurs in the real world. It is a term that should be eliminated along with reliability predictions of electronics systems with no moving parts. Continue reading Eliminating early life failures
Posted 12-11-2012 by Kirk Gray,
Accelerated Reliability Solutions, L.L.C.
“When the number of factors coming into play in a phenomenological complex is too large, scientific method in most cases fails. One need only think of the weather, in which case the prediction even for a few days ahead is impossible.” ― Albert Einstein
“Prediction is very difficult, especially about the future.” – Niels Bohr* We have always had a quest to reduce future uncertainties and know what is going to happen to us, how long we will live, and what may impact our lives. Horoscopes, Tarot
Continue reading Electronics Failure Prediction Methodology does not work
This post is a conversation first held on the LinkedIn group No MTBF. I’m capturing a portion of the contributions here to continue the discussion or to widen the audience. Reminds me of always assuming 95% confidence is the right value when designing a test, or assuming constant failure rate. So, let the conversation continue, starting with the original post. Continue reading Where does 0.7eV come from
The term Bayesian Reliability Analysis is popping up more and more frequently in the reliability and risk world. Most veteran reliability engineers just roll their eyes at the term. Most new reliability engineers dread the thought of having to learn something else new, just when they are getting settled in the job. Regardless, it is a really good idea for all reliability engineers to have a basic understanding of Bayesian Reliability Analysis.
This series explains Bayesian Reliability Analysis and justifies Continue reading What’s All the Fuss about Bayesian Reliability Analysis?
Great note [response to comment on Drain in the Bathtub Curve on NoMTBF Linkedin Group] – yes, there is a place for parts count prediction — not to determine the mtbf, to encourage proper derating, thermal engineering, and parts reduction, etc. It’s a start and as you note only one part of the reliability program. Continue reading Role of parts count prediction
Most reliability engineers are familiar with the life cycle bathtub curve, the shape of the hazard rate or risks of failure of a electronic product over time. A typical electronic’s life cycle bathtub curve is shown in figure 1. Continue reading Why The Drain in the Bathtub Curve Matters
Just a short post to point to a newly added paper to the reference section. A few years ago I recalled seeing a paper that studied the difference to expect between various parts count methods and actual results. Continue reading Parts count variation
Historically Reliability Engineering of Electronics has been dominated by the belief that 1) The life or percentage of complex hardware failures that occurs over time can be estimated, predicted, or modeled and 2) Reliability of electronic systems can be calculated or estimated through statistical and probabilistic methods to improve hardware reliability. The amazing thing about this is that during the many decades that reliability Continue reading No Evidence of Correlation: Field failures and Traditional Reliability Engineering