What is Reliability?

It’s not MTBF. It’s not just the period of time the product does not fail. It’s not just a probability.

It’s a bit more. Reliability is it ‘just works’.

HP calculators are reliable. They work and keep on working. Apparently Lexus makes reliable cars. (According to the current car rankings by Consumer Reports, 2015). My coffee maker is reliable.

The dictionary on my Mac says reliable is:

And, according to O’Connor and Kleyner in Practical Reliability Engineering, 5^th ed. Reliability is:

The probability that an item will perform a required function without failure under stated conditions for a stated period of time.

This is a definition we can use as engineers. It has four parts:

• Function
• Environment
• Probability
• Duration

And we certainly can define and measure each well.

BTW: MTBF is only probability (actually stated as an inverse failure rate), thus does not fully define reliability.

Consistent, trustworthy? Yes, a reliable product or system should process these essential qualities, too.

Reliability conjures many images and thoughts. The examples you envision are different than mine. That is fine. The concept remains the same. When an item is reliable, it just works. I like to add that it just keeps on working.

When setting goals, estimating, predicting, or measuring reliability, use all four element of the definition laid out by O’Connor and Kleyner. Be clear and complete. Keep it simple and make it reliable.

What comes to mind when you think of reliability? Leave a comment and share what you consider reliable.

The MTBF Conspiracy Theory

When my son was young he asked a lot of questions that were difficult to answer. For example:

• Why is the sky blue?
• Why do I have to go to school?
• What is a conspiracy theory?

The first two were expected, yet the third set me back a little. How do you explain conspiracy theory to a 5^th grader? The dictionary type definitions just seemed to confuse everyone. So, I made up a conspiracy theory.

I said, “Did you know, North Dakota, is not really a state?”

For those that haven’t heard of North Dakota, which on many maps is in the north central part of the US, that just reinforces the theory that it doesn’t exist.

My son, having recently memorized all fifty US states and their capital cities in school, said I was wrong and he even knew that was true as he still recalled the capital city name.

“Prove it.”, Was all I said in response.

“Well it’s on the map on the country as a state.” My reply included how maps change and are arbitrary. Anyone could have drawn the map, and how do we know it is accurate. Maybe the good folks in South Dakota paid the map maker to draw in the fictions state of North Dakota.

“It’s listed in Wikipedia!” And, my reply, was about how anyone can create a posting on the site, what is the proof it’s actually true? Have you ever seen a car with ND plates or meet someone from there?” He hadn’t.

My son knew I was only demonstrating the idea of a conspiracy theory. We had fun with it for years.

I was glad he never asked me,

“Why do people use MTBF?”

Just with the blue sky, a shrug and smile just wasn’t a good enough answer. There has to be a rational reasons people use MTBF.

After writing about perils of MTBF use for a few years, my current theory is it has to be a conspiracy.

The MTBF conspiracy theory revealed

Here’s what I think happened.

A bright engineer was tasked with estimating the reliability of a nuclear submarine’s electronics. He was given about a month to achieve this task, which is not enough time to conduct any testing. So, he gathered all the component failure rate data, tallied it up and reported the expected failure rate. {Parts count prediction}

The marketing department noticed the failure rate value and the word failure. The admission that the submarine might fail didn’t help to sell summaries, so they flipped the failure over, creating the average time between failure, or mean time between failures, MTBF.

The lower the failure rate the higher the MTBF went. Up was good. Failure is bad. {That’s how I think marketing folks think – sorry}

The engineers understood failure rates the math to create MTBF was pretty simple. So whatever, tis the same thing. Then management got involved.

The management team only wanted to read and talk about MTBF {again the word ‘failure ’  is bad thinking}. They set MTBF goals, they expected glowing reports of increasing MTBF values, and so on.

Then something really bad happened.

The US Military created a standard. And, a company used a computer to automate the standard’s estimate  of MTBF. Other’s did too. Now there was profit to be made by estimating MTBF, not reliability. So, they sold MTBF estimations. After all, that is what the management team wants, MTBF.

The military standard spawned many industry standards. The standards become parts of purchase contracts. MTBF flourished.

“What is your MTBF?” became an acceptable way to ask about reliability performance.

The murky bit of the theory involves why very few stood up to say, “Let’s not use MTBF, it is not very useful. Let’s use the probability of success over a duration (reliability) instead.” You may have said these very words or words to the same affect. And you felt the resistance.

• We always use MTBF.
• Everyone in our industry uses MTBF.
• The vendor only provides MTBF values.

My theory is we all know better, {maybe not the marketing folks – sorry} and we just do feel able to overcome the resistance to change. We know we could do much better with better metrics, yet the backlash is unrelenting.

Just as that first engineer figured out a quick way to come up with a failure rate estimate, we too face the necessity to use MTBF. We do not have the time or energy to change our company or industry to stop using MTBF. So, we just do it.

It’s easy.

I don’t know if the spread of MTBF use is organized by a secret group or not. I suspect not. Yet the ease of use and avoidance of the word failure (or anything the smells like we would have to do statistics) conspired to trap us into using MTBF.

That’s my theory. If you know of any critical bits of information to support this theory, let me know. If we expose the conspiracy for what it is, it may just fade away. We then may get back to work doing reliability engineering and creating reliable products.

Just a quick note. A good friend forwarded me a course listing with the note, “This is not the traditional MTBF course, despite the title.” or something like that.

So, looked into the course offered by The MIRCE Akademy, titled Mean Time Between Failures — MTBF: Scientific method for the accurate predictions of Mean Time Between Failures.

Looks interesting.

So, this may be a good one to take. You can find more information at the course page.

If you have or plan on taking the course, please leave a comment here to let others know how it went and what you learned.

Just Because the Customer Requests MTBF

Is that justification to use MTBF?

No.

It’s not. In this case the customer is probably not asking for MTBF, what they most like want to know is something meaningful about the expected reliability performance of the item in question. They want to know if what they will or did purchase will last as long as they expect. Continue reading Just Because the Customer Requests MTBF →

MTBF is a Statistic, Not the Only One

We often face just a sample of life data with the request of estimating the reliability of the system. Or, we have a touch of test results and want to know if the product is reliability enough, yet. Or, we gather repair times to grapple with spares stocking.

We need to know the reliability. We need to know the number.

MTBF (or close cousin MTTF) is just that number. It is easy to calculate. A higher number means the system is more reliable. And, the metrics are in the units of time, often hours, which is easy to understand (and misunderstand).

In early chapters of reliability engineering books, or in introduction to reliability, we learn about the exponential distribution and the population parameter, theta. We also learn about the sample statistics which provides an unbiased estimated for the population parameter. In both cases, MTBF, or the mean time between failure, is the one value we have to master.

Other Statistics

Reliability is pretty easy using just one statistic. One calculation, one number, and we’re done.

Then a couple of things start to happen.

First, we notice that the actual time to failure behavior is not predicted, nor follows, the expected pattern when using just MTBF and the exponential distribution. The average time to fail changes as the system ages. We find that we run out of spares based on calculations using MTBF as the parts fail more and more often.

Second, we learn just a little more. We turn the page in the book or attend another webinar. We hear about another distribution commonly used in reliability engineering. The Weibull distribution. But, wait, hold on there. The Weibull has two and sometimes three parameters. I’ll need to learn about plotting, censored data, regression analysis, goodness of fit, confidence intervals, and a bunch of statistical methods.

Life was good with just one statistic.

We didn’t sign up to be reliability statisticians.

Well, too bad.

Actually, when using even just the one statistic, MTBF, we also should have been

• Checking assumptions
• Fitting the data to the exponential distribution function
• Evaluating the goodness of fit
• Calculating confidence bounds
• And, using those other statistical methods

In order to understand and use our sparse and expensive datasets, we need to use the tools found in the statistics textbooks.

Yes, the Weibull distribution has two or three parameters, thus we need to evaluate how well our statistics describe the data in a more rigorous way. And, we learn so much more. For example, we can model and predict a system with decreasing or increasing failure rates over time. We can estimate the number of required spares next year with a bit more accuracy then using just MTBF.

There are more benefits. Have you advanced past the basic introduction and embraced the use of reliability statistics? How’s it going and what challenges are you facing?