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.
One technique to calculate a product’s MTBF is to count the number of failures and divide by the tally of operating time.
You already know, kind reader, that using MTBF has its own perils, yet it is done. We do not have to look very far to see someone estimating or calculating MTBF, as if it were a useful representation of reliability… alas, I digress.
Counting failures would appear to be an easy task. It apparently is not. [Read more…]
If you have been a reliability engineer for a week or more, or worked with a reliability engineer for a day or more, someone has asked about testing planning. The conversation may have started with “How many samples and how long will the test take?”
You have heard the sample size question.
Did you notice the speed limit signs in your neighborhood today?
If like me, you went about your commute or regular travels relatively blind. You watched for the neighbor’s dog that jumped into the road last week, yet didn’t register seeing the speed limit sign.
It’s a cognitive burden to notice the mundane or known. [Read more…]
This may seem an odd article for the NoMTBF site. Stay with me for a moment longer.
Over the years of speaking out on the perils of MTBF, there has been some pushback. A few defend using MTBF. Here are three of the most common (maybe not exactly the best, per se) reasons to use MTBF. [Read more…]
Nearly everyone I’ve ever met doesn’t want or like their toaster to fail.
It will, and that is a bummer, as the quick and easy way to warm up the morning toast will be thwarted.
Failures happen. As reliability engineers, we know that failures happen. Helping others to identify potential failures, to avoid failures or to minimize failures is what we do best.
It is our ability to teach others about reliability engineering that allows us to be successful. [Read more…]
Some years ago a few colleagues compared notes on results of a Weibull analysis. Interesting we all started with the same data and got different results.
After a recent article on the many ways to accomplish data analysis, Larry mentioned that all one needs is shipments and returns to perform field data analysis.
This got me thinking: What are our common methods and sets of results when we perform life data analysis? [Read more…]
I got this question the other day. The person knew about the NoMTBF campaign. They didn’t quite understand why it was a big deal, especially for me, to avoid MTBF.
The tiff between MTBF and myself is not personal. The metric has not been a part of my work or caused any significant problems for me personally.
It has caused problems that have caused problems for my enjoyment of products and systems though. It has lead to poor decisions by many organizations that create items I and you use on a regular basis.
We can do better than to settle with the use of MTBF in our own work or in the work of those around us. Here are 10 reasons I recommend you avoid using MTBF.
Recently, Ziad let me know he published an article titled Analyzing Repairable System Failures Data in the April-May 2017 issue of Uptime magazine (subscription required). He suggested I’d be interested in the article since it provides a way to analyze repairable system data without using MTBF. He was right.
The article is a short description and tutorial on using mean cumulative plotting and function (MCF). While the article recommends staying away from using MTBF, it could be a bit of a stronger message. The article does provide a very nice worked out example illustrating the use of a mean cumulative plot. [Read more…]
I had an interesting case study a couple weeks ago, where “I’m giving you what you want, not what you asked for “ when the requirement as usual was a blanket MTBF, but the product design elements clearly indicated wearout could / would be a factor. — Kevin
Creating a product or system that lasts as long as expected, or longer, is a challenge.
It’s a common challenge that reliability engineering and the entire engineering team face on a regular basis. It’s also not our only challenge.
We face and solve a myriad of technical, political, and engineering challenges. Some of our challenges are born and carried forward by our own industry. We have tools suitable for a given purpose altered to ‘fit’ another situation (inappropriately and creating misleading results). We have terms that we, and our peers, struggle to understand.
Sometimes, we, as reliability engineers, have set up challenges that thwart our best efforts to make progress.
Let’s examine a few of the self made challenges and discuss ways to overcome these obstacles permitting us to tackle the real hurdles in our path. [Read more…]
MTBF is a magic method for predicting time to failure for your new design. On this page we present to you the fastest way to achieving MTBF.
Maybe The Best Function ever! [Read more…]
Is there any useful result from a parts count prediction?
In most cases that I’ve seen parts count predictions used they are absolutely worthless. Worse, is the folks receiving the results believe they are accurate estimates of reliability performance (or at least use the results as such).
In my opinion, the range of parts count prediction methods and databases harm the field of reliability engineering.
We need to call out the poor results, promote better practices, and stop the vapid use of such a poorly understood tool. [Read more…]
A conversation the other day involved how or why someone would use the mean of a set of data described by a Weibull distribution.
The Weibull distribution is great at describing a dataset that has a decreasing or increasing hazard rate over time. Using the distribution we also do not need to determine the MTBF (which is not all that useful, of course).
Walking up the stairs today, I wondered if the arithmetic mean of the time to failure data, commonly used to estimate MTBF, is the same as the mean of the Weibull distribution. Doesn’t everyone think about such things?
Doesn’t everyone think about such things? So, I thought, I’d check. Set up some data with an increasing failure rate, and calculate the arithmetic mean and the Weibull distribution mean. [Read more…]
A habit you should examine and stop.
At first, I wondered if MTBF use was addictive, yet I thought that comparison would belittle the very serious issues of those with addictive behaviors. Using MTBF does not generally cause a person harm, while poor decision based on it might harm the organization.
I find those that regularly employ MTBF do so without thinking about it too much. If someone mentions reliability, they think MTBF. Automatically.
Habits help us reduce cognitive load and make our life simpler. For example, do you need to focus on how to put on your shoes every morning? I’m personally happy my habit skills allow me to remember how to drive safely without the intense focus required the first time I got behind the wheel.
Let’s examine how to tell if someone has the Habit of MTBF use and what you can do about it. [Read more…]
MTBF is not meant to be used for anything other than teaching someone new to reliability how the various functions and tasks work.
Using MTBF in the real world is an oversimplification to the point of being less then useful. Possibly even harmful.
You see MTBF is books, articles, and papers, often with the caveat of the assumption to simplify the math to illustrate the process or concept. Hence, does not apply for actual use. [Read more…]
