MTBF is a symptom of a bigger problem. It is possibly a lack of interest in reliability. Which I doubt is the case. Or it is a bit of fear of reliability.

Many shy away from the statistics involved. Some simply do not want to know the currently unknown. It could be the fear of potential bad news that the design isn’t reliable enough. Some do not care to know about problems that will require solving.

Whatever the source of the uneasiness, you may know one or more coworkers who would rather not deal with reliability in any direct manner.
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To me it means very little, as it rarely occurs. Products fail for a wide range of reasons and each failure follows it’s own path to failure.

As you may understand, some failures tend to occur early, some later. Some we call early life failures, out-of-box failures, etc. Some we deem end-of-life or wear-out failures. There are a few that are truly random in nature, just as a drop or accident causing an overstress fracture, for example. [Read more…]

Just back from the Reliability and Maintainability Symposium and not happy. While there are signs, a proudly worn button, regular mentions of progress and support, we still talk about reliability using MTBF too often. We need to avoid MTBF actively, no, I mean  aggressively.

Let’s get the message out there concerning the folly of using MTBF as a surrogate to discuss reliability. We need to work relentlessly to avoid MTBF in all occasions.

Teaching reliability statistics does not require the teaching of MTBF.

Describing product reliability performance does not benefit by using MTBF.

Creating reliability predictions that create MTBF values doesn’t make sense in most if not all cases. [Read more…]

MTBF use and thinking is still rampant. It affects how our peers and colleagues approach solving problems.

There is a full range of problems that come from using MTBF, yet how do you spot the signs of MTBF thinking even when MTBF is not mentioned? Let’s explore the approaches that you can use to ferret out MTBF thinking and move your organization toward making informed decisions concerning reliability. [Read more…]

Over 20 years ago the Assistant Secretary of the Army directed the Army to not use MIL HBK 217 in a request for proposals, even for guidance. Exceptions, by waiver only.

217 is still around and routinely called out. That is a lot of waivers.

Why is 217 and other parts count database prediction packages still in use? Let’s explore the memo a bit more, plus ponder what is maintaining the popularity of 217 and ilk.

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Why do we use ReliaSoft instead of JMP to Identify the Time to Failure?

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

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Lifetime Evaluation vs. Measurement. Part 3.

Sometimes shifting your perspective
is more powerful than being smart.

—Astro Teller

Guest post by Oleg Ivanov

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…]

Lifetime Evaluation vs. Measurement. Part 2.

Guest post by Oleg Ivanov

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…]

Lifetime: Evaluation vs. Measurement

Guest Post by Oleg Ivanov

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…]

Futility of Using MTBF to Design an ALT

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…]

MTBF Paradox: Case Study

Guest Post by Msc Teofilo Cortizo

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…]

A Novel Reason to Use MTBF

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…]