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NoMTBF

A series of articles devoted to the eradication of the misuse of MTBF.

ISSN 2168-4375

Plus, we explore other commonly misused or misunderstood reliability-related topics and what one should do instead. A little understanding will help you get better results with your efforts.

Note: This is a reposting with editing, updating, etc. of the articles that first appeared at NoMTBF.com.

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The Business of Providing MTBF

The Business of Providing MTBF

What Price Providing MTBF?

What good is your service if your livelihood consists of providing MTBF upon request?

Sure, you earn some money, yet did the customer receive value in the transaction? As you know, or should know, MTBF is so commonly misunderstood that the customer is likely to be confused about what they want, reliability or MTBF. Providing them with MTBF does not answer their question.

Worse, the customer thinks they got something of value and blithely heads off with rather meaningless information.

My contention is to provide MTBF because the customer’s request is wrong. We know better. Those performing predictions, doing data analysis, and other reliability engineering work know that MTBF is a faulty and rather meaningless metric often confused with reliability, R(t). (probability of success over a duration). [Read more…]

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The Many Ways of Data Analysis

The Many Ways of Data Analysis

Given Some Data, Do Data Analysis

Let’s say we have a set of numbers, {2.3, 4.2, 7.1, 7.6, 8.2, 8.4, 8.7, 8.9, 9.0, 9.1} and that is all we have at the moment.

How many ways could you analyze this set of numbers? We could plot it a few different ways, from a dot plot, stem-and-leaf plot, histogram, probability density plot, and probably a few other ways as well. We could calculate a few statistics about the dataset, such as mean, median, standard deviation, skewness, kurtosis, and so on. [Read more…]

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High MTBF with Low Reliability

High MTBF with Low Reliability

Can You Have a High MTBF and Low Reliability?

As regular readers know, MTBF by itself is misleading. It can also be deceptive when representing actual data. Just because you have a high MTBF value doesn’t mean it is reliable.

In a previous article, 10 Reasons to Avoid MTBF, I mentioned that it is possible to have a relatively high MTBF value when the actual reliability is low. Ashley sent me the following note:

Hi Fred, i love reading your articles they are very informative. I have a question about something you said in a comment which i am hoping you will be able to clarify for me. You said products with higher MTBF can actually be less reliable than products with a lower MTBF

I have tried to find information on how this is possible online, and tried to do the maths myself to make this happen but i have to admit i am struggling.

No worries, Ashley, let’s work out an example to illustrate what I meant. [Read more…]

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How About Weibull Instead of MTBF?

How About Weibull Instead of MTBF?

What About Weibull? Can I Use it Instead of MTBF?

This was a follow-up question in a recent discussion with Alaa concerning using a metric other than MTBF.

The term ‘Weibull’ in some ways has become a synonym for reliability. Weibull analysis = life data (or reliability) analysis. The Weibull distribution has the capability to describe a changing failure rate, which is lacking when using just MTBF. Yet, is it suitable to use ‘Weibull’ as a metric? [Read more…]

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Use Lognormal Distribution

Use Lognormal Distribution

The lognormal distribution has two parameters, μ and σ. These are not the same as mean and standard deviation, which is the subject of another post, yet they describe the distribution, including the reliability function.

$$ \displaystyle R(t)=1-\Phi \left( \frac{\ln (t)-\mu }{\sigma } \right)$$

Where Φ is the standard normal cumulative distribution function, and t is time. [Read more…]

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Arrhenius or Erroneous

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…]

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Acceleration Factors

Acceleration Factors

Temperature acceleration factor for ALT planning (question posted to Linkedin Society of Reliability engineers group, 5/7/12

Hello, can anyone advise me how to calculate temperature acceleration factor for a complex system including cards, RF elements, cables, motors and moving parts? Is the Arrhenius model valid for such systems, or there are more precise models? Thank you!

[Read more…]

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No Evidence of Correlation: Field failures and Traditional Reliability Engineering

No Evidence of Correlation: Field failures and Traditional Reliability Engineering

Historically, Reliability Engineering of Electronics has been dominated by the belief that 1) The life or percentage of complex hardware failures that occur over time can be estimated, predicted, or modeled, and 2) the 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

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Shaping Organizational Behavior

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.

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What Should We Use Instead of MTBF?

What Should We Use Instead of MTBF?

Giving a presentation last week and asked if anyone uses an 85%RH/85°C type test, and a couple indicated they did. I then asked why.

The response was – just because. We have always done it, or it’s a standard, or customers expected it. The most honest response was, ‘I don’t know’.

Why is the test being done? Who is using the information for a decision? What is the value of the test results? If ‘just because’ is the best you can say about a test, why do it?

[Read more…]