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.
This is a question someone posted to Quora and the system prompted me to answer it, which I did.
This question is part of the general question around which software tools do you use for specific situations. First, my response to the question. [Read more…]
Give me a place to stand on, and I will move the Earth.
Archimedes
Its known HALT is an effective way to find the weaknesses in your product during the reliability improvement program. In doing so, we view HALT as a qualitative test only. We cannot define the reliability and lifetime of the product from its results. So, unfortunately, we cannot use HALT for purposes of Type Certification, confirm the lifetime of Critical Parts, predict the warranty and maintenance costs, which are required, for example, for aviation.
If we could combine the effectiveness of HALT (high acceleration of testing) with the benefits of quantitative testing, we would get a very powerful tool for the Reliability Demonstration and the Reliability Development of the new products.
Sometimes shifting your perspective
is more powerful than being smart.—Astro Teller
A common approach for “no failure” testing is the use of the well-known expression
$$ (1) \quad 1-CL={{R}^{n}}$$
where CL is a confidence level, R is a required reliability, n is a sample size. Its parent is a Binomial distribution with zero failures. This expression is like a poor girl: [Read more…]
A result of life testing can be measurement or evaluation of the lifetime.
Measurement of the lifetime requires a lot of testing to failure. The results provide us with the life (time-to-failure) distribution of the product itself. It is long and expensive.
Evaluation of the lifetime does not require as many test samples and these tests can be without failures. It is faster and cheaper [1]. A drawback of the evaluation is that it does not give us the lifetime distribution. The evaluation checks the lower bound of reliability only, and interpretation of the results depends on the method of evaluation (the number of samples, test conditions, and the test time). [Read more…]
How can we tell whether an iron is hot enough? The answer is obvious: We can measure temperature by using a thermocouple and a meter. But, in practice, we lick our finger and touch the iron. Sizzle…. Yes, it’s hot!
We know a priori the boiling temperature of water and we can evaluate the temperature of the iron. This method has a lower cost. [Read more…]
Let’s say we want to characterize the reliability performance of a vendor’s device. We’re considering including the device within our system, if and only if, it will survive 5 years reasonably well.
The vendor’s data sheet lists an MTBF value of 200,000 hours. A call to the vendor and search of their site doesn’t reveal any additional reliability information. MTBF is all we have.
We don’t trust it. Which is wise.
Now we want to run an ALT to estimate a time to failure distribution for the device. The intent is to use an acceleration model to accelerate the testing and a time to failure model to adjust to our various expected use conditions.
Given the device, a small interface module with a few buttons, electronics, a display and enclosure, and the data sheet with MTBF, how can we design a meaningful ALT? [Read more…]
“What’s the MTBF of a Human?” That’s a bit of a strange question?
Guest post by Adam Bahret
I ask this question in my Reliability 101 course. Why ask such a weird question? I’ll tell you why. Because MTBF is the worst, most confusing, crappy metric used in the reliability discipline. Ok maybe that is a smidge harsh, it does have good intentions. But the amount of damage that has been done by the misunderstanding it has caused is horrendous.
MTBF stands for “Mean Time Between Failure.” It is the inverse of failure rate. An MTBF of 100,000 hrs/failure is a failure rate of 1/100,000 fails/hr = .00001 fails/hr. Those are numbers, what does that look like in operation? [Read more…]
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.
