Fred Schenkelberg — Thought Leader

Author of CRE Preparation Notes, Musings", NoMTBF, multiple books & ebooks>, co-host on Speaking of Reliability>/a>, and speaker in the Accendo Reliability Webinar Series.

This author's archive lists contributions of articles and episodes.

by Fred Schenkelberg Leave a Comment

What is the MTBF Means?

Guest post by Msc Teofilo Cortizo

The term MTBF (Mean Time Between Failures) within maintenance management, it is the most important KPI after Physical Availability. Unlike MTTF (Mean Time To Failure), which relates directly to available equipment time, MTBF also adds up the time spent inside a repair. That is, it starts its count from a certain failure and only stops its counter when this fault was remedied, started and repeated itself again. According to ISO 12849: 2013, this indicator can only be used for repairable equipment, and MTTF is the equivalent of non-repairable equipment. [Read more…]

by Fred Schenkelberg 1 Comment

Consider the Decision Making First

Reliability activities serve one purpose: to support better decision making.

That is all it does. Reliability work may reveal design weaknesses, which we can decide to address. Reliability work may estimate the longevity of a device, allowing decisions when compared to objectives for reliability.

Creating a report that no one reads is not the purpose of reliability. Running a test or analysis to simply ‘do reliability’ is not helpful to anyone. Anything with MTBF involved … well, you know how I feel about that. [Read more…]

by Fred Schenkelberg 3 Comments

What is Wrong With Success Testing?

Three prototypes survive the gauntlet of stresses and none fail. That is great news, or is it? No failure testing is what I call success testing.

We often want to create a design that is successful, therefore enjoying successful testing results, I.e. No failures means we are successful, right?

Another aspect of success testing is in pass/fail type testing we can minimize the sample size by planning for all prototypes passing the test. If we plan on running the test till we have a failure or two, we need more samples. While it improves the statistics of the results, we have to spend more to achieve the results. We nearly always have limited resources for testing.

Let’s take a closer look at success testing and some of the issues you should consider before planning your next success test. [Read more…]

by Fred Schenkelberg 9 Comments

Defining a Product Life Time

An Elusive Product Life Time Definition

The following note and question appear in my email the other day. I had given the definition of reliability quite a bit of thought, yet have not really thought too much about a definition of ‘product life time’.

So after answering Najib’s question I thought it may make a good conversation starter here. Give it a quite read, and add how you would answer the questions Najib poses. [Read more…]

by Fred Schenkelberg Leave a Comment

Life Data Analysis with only 2 Failures

Life Data Analysis with Only 2 Failures

Here’s a common problem: You have been tasked with peering into the future to predict when the next failure will occur.

Predictions are tough.

One way to approach this problem is to analyze the history of failures of the most typical system. The issue looms larger when you have only two observed failures from the population of systems in question.

While you can fit a straight line to two failures and account for all the systems that operated without failure, it is not very satisfactory. It is at best a crude estimate.

Let’s not consider calculating MTBF. That would not provide useful information as regular readers already know. So what can you do given just two failures to create a meaningful estimate of future failures? Let’s explore a couple of options. [Read more…]

by Fred Schenkelberg 6 Comments

Reliability Predictions

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.

[Read more…]

by Fred Schenkelberg Leave a Comment

Calculating System Availability

How to Properly Calculate System Availability

Recently received a request for my opinion concerning the calculation of system availability using the classic formula

$$ \displaystyle \large A=\frac{MTBF}{MTBF+MTTR}$$

The work is to create a set of goals for various suppliers and contractors to achieve. The calculation values derive from vendor data sheets and available information concerning MTBF and MTTR. The project is in the design phase; thus, they do not have working systems available to measure actual availability.

How would you go about improving on this approach? [Read more…]

by Fred Schenkelberg 13 Comments

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…]

by Fred Schenkelberg Leave a Comment

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…]

by Fred Schenkelberg Leave a Comment

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…]

by Fred Schenkelberg 18 Comments

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…]

by Fred Schenkelberg Leave a Comment

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…]

by Fred Schenkelberg Leave a Comment

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…]

by Fred Schenkelberg Leave a Comment

Culture Still Eats Strategy for Breakfast

Guest Post by Bill Pomfret (first posted on CERM ® RISK INSIGHTS – reposted here with permission)

It took us years to move from top-down to people-centric management, but the journey was worth it Peter Drucker once said, “Culture eats strategy for breakfast.” Throughout my career, I have spent a lot of time in the strategy room. But no matter what we decided there, until we got the organizational culture right, we were unsuccessful in executing our goals.

[Read more…]

by Fred Schenkelberg

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…]