Your Reliability Engineering Professional Development Site
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.
The MTBF calculation is widely used to evaluate the reliability of parts and equipment, in the industry is usually defined as one of the key performance indicators. This short article is intended to demonstrate in practice how we can fool ourselves by evaluating this indicator in isolation. [Read more…]
Thanks to a reader that noticed my question on why MTBF came into existence, we have a new (new to me at least) rationale for using MTBF. Basically, MTBF provides clarity on the magnitude of a number, because a number in scientific notation is potentially confusing.
What is doubly concerning is the use of MTTF failure rate values in ISO standards dealing with system safety.
Let’s explore the brief email exchange and my thoughts. [Read more…]
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…]
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…]
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…]
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…]
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…]
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.
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…]
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…]
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…]
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…]
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…]
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…]
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…]