What are the Other Challenges in Reliability

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…]

Get Your MTBF Estimation Here

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…]

Why Would You Do a Parts Count Prediction?

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…]

MTBF is Just the Mean, Right?

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…]

With Enough Reinforcement, MTBF Use Becomes a Habit

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 a Starting Point, Only

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…]

Reliability is about making the right decision, each time.

Answering Questions

A common role during a first assignment as a reliability engineering is to answer a question or accomplish a task. It may help someone to make reliability decisions.

We may be asked, as I was, how long will this new product function during use? The director of engineering wanted to know if the new design was reliable enough to meet the customer’s requirements concerning reliability. He didn’t ask it that way, yet he did have a question that needed answering.

Sometimes we run a batch of tests, conduct failure analysis on field returns, or compare the durability to two vendor subsystems. In each case, there is a question to be answered.

A decision to be made by someone else. [Read more…]

When my car fails to start, as a customer I only know that my car didn’t start.

When my phone fails to turn on, or the dishwasher leaks, or the printer jams, I only know I’ve experienced an unwanted outcome.

I really do not care, at the moment, why the coffee maker is not producing my morning cup of coffee. My first thought is ‘now where do I find a cup of coffee?’ As a reliability engineer I’m naturally curious about what caused the failure and can I fix it immediately to get the morning cup brewing.

My thinking does not classify the failure or the source of the failure as a quality or reliability problem. Then why is it that some organizations split reported field failures thus? [Read more…]

Jobs at Apple has done it. You can, too.

Change an industry. The advent of iTunes and iPods forever changed how the world buys and listens to music.

While Jobs had the resources of Apple to help make the change happen. It still started as an idea (may or may not have been Jobs’ idea, I don’t know). It grew and created enough momentum to effect a change across an entire industry.

Change is hard.

If you have tried to help your team move in a new direction or consider the reliability risks present in the current design, then you know change is difficult to make happen. You most likely have been successful a few times, and not a few also. I know I’ve crashed into the rocky spit more often than I can count. [Read more…]

The Variety of Statistical Tools to Support Your Decision Making

My wife and I moved to a new home last year. We have yet to organize our tools.

The bedroom and kitchen are now organized. We, for the most part, can find the sweater or pan that we’re seeking.

No so for our tools in the shop. We have an assortment of hand tools for painting, home maintenance, yard work, and woodworking. In our previous home, we had the tools on pegboards, on shelves, in cabinets. We could find the right tool for the job at hand quickly. We’ve avoided the tool aisle at the hardware store recently, as we were sure we had the tool we need in the jumbled mess in our garage already. Still haven’t found it, though.

Have you noticed the number of statistical tools available? It’s like visiting a well-stocked tool store. There are basic tools like trend charting and advanced tools like proportional hazard models. Let’s explore the available tools a little so you can quickly find the right tool for the question or problem you are facing today.

We are well aware of cases when Accelerated Testing is successfully creating by statisticians, here I would like to propose an engineer’s approach.

We have created a new product, there is (is) an order for it (50 products at a price of $200). Can we start manufacturing and delivery? No, we can’t. We are stopped by significant warranty obligations and lack of confidence in the reliability of the product during the warranty period.

This is a typical task with uncertainty.

Two people have shaped how I guess an answer.

Their comments and guidance have tailored how to form a quick estimate, my ability to articulate a hunch and the effectiveness of those guesses.

You probably guess or make a rough estimate regularly. How good is your gut feel? Do you keep track and score yourself?

Making an estimate should be second nature for you. It’s not something to do in public, too often. The practice can aid you in numerous ways. [Read more…]

Do make compromises around gathering and analyzing data since you only need to report MTBF?

Do you use MTBF (exponential distribution) based test planning when you know the product has a non-constant hazard rate?

These questions came up this week via email looking for advice when directed to ignore the actual situation and just do what the customer wants.

I’m traveling this week, rather jet-lagged today, so going to keep this one short.

How would you answer these questions? What advice would you give someone using exponential based reporting, test planning, or data analysis approaches knowing the customer expects that process yet the data and your experience suggest you should use another method  (Weibull or MCF, for example)?

Please add you comments below and let’s prepare a list of what one should say or use to respond to such actions.