Your Reliability Engineering Professional Development Site
Let's have an in-depth discussion during the live webinar series events: Reliability Engineering Basics, Statistics, Accelerated Testing, Program Assessment, and Improvement. Catch up or review past events below.
To attend an upcoming live event, visit the Live Events section of the site, which lists the available upcoming webinars. Recordings are available via recorded videos or audio only via the podcast show.
Find one that looks interesting and register for the event. You will receive login directions and reminders prior to the event. Plus you will be able to join the discussion during the event.
One of the things I like about reliability engineering is the field has a stable set of tools and approaches with an ever-changing set of challenges. The process of conducting an FMEA or Root Cause Analysis hasn’t changed much over time. Meanwhile, with the advent of new designs, materials, assembly techniques, and higher customer expectations, we continue to face interesting problems. [Read more…]
This is the fourth in our webinar series, ‘Reliability Analysis … now what?’ We have examined how to turn data points (like the different failure points of devices under test) into something simple, useful, and sometimes beautiful! This is something that commercial software can’t do. [Read more…]
Risk is uncertainty. Will this work? How will this item fail? Is it safe? Risk is not limited to product failure, including brand topics such as market acceptance, regulatory approval, supply chain stability, international conflicts, and more. Reliability engineering tends to focus on product failure to perform as expected, and we must fit into the large context of risks facing any product or system. [Read more…]
This is the third in our webinar series ‘Reliability Analysis … now what?’ The first webinar showed what it means to turn reliability data into something a decision-maker can use. Not a chart. Not a graph. Not a ‘p’ value. Not anything else that you find in a textbook but isn’t useful. Last month’s webinar talked about how the best way to do this is to use computers … but in an easy way! And we talked about creating a ‘posse’ of possible ‘ways’ to explain the data we see. And this webinar is about how we use this thing called Markov Chain Monte Carlo Simulation (MCMC) to create this ‘posse.’ And the ‘posse’ isn’t random … it is based on the ‘likelihood’ of each one being the correct one, which is how MCMC works. [Read more…]
The goal of this presentation is to educate the audience on options to prevent electronic equipment failure used in enclosed spaces from corrosion through preventative maintenance practices. As most know, the metals used in today’s electronics are processed but want to turn back to their original state as the iron components will oxidize, and the corrosion process begins. Adding humidity, vibration, temperature swings, and acid environments (such as being in a tire plant or downwind from a refinery) will accelerate the process. It is this corrosion and subsequent weakening or disfiguring of the metal that causes malfunctions and failures [Read more…]
Dick Moss, retired HP Corporate Quality and Reliability Manager, once mentioned there are only five equations necessary to be a good reliability engineer. He quickly listed four equations and, with a smile, said the fifth one is the one you need to solve the current problem. [Read more…]
Last month’s webinar was ‘Reliability Analysis … now what?’ And we showed how to slightly ‘tweak’ all those textbook reliability analyses into USEFUL ACTIVITIES. What good is a 90 % confidence bound on reliability? What does this mean for profit? Or fleet size? Or mission success? We solved this problem by identifying the ‘likely’ ways we can explain our data. In fact – there is a whole bunch of statistics on creating the ‘likelihood’ of an explanation of what we see. We use this ‘likelihood’ in most statistical applications – including those USEFUL ACTIVITIES we discussed above. [Read more…]
The difficult part of creating this list of essential techniques is to avoid selecting just the most common. There is an overlap between essential and those commonly used, yet essential implies a technique is crucial—crucial in rare yet very important situations. The idea for today’s discussion is to focus on those vital few techniques that every reliability engineer must master. [Read more…]
Let’s say that you have some ‘reliability guys’ do some reliability analysis on reliability data … and they give you some numbers. Or a plot. Or a curve. Perhaps you are the reliability guy … and you do the analysis – and then what? [Read more…]
Warranty returns are a great place to start for setting targets for new products. But how do you translate that to specific numbers to design to? If you know the strength of a product and the return rate, you can develop stress profiles. If you have multiple similar products, your estimates can be even better. From the profiles, targets can be developed for future products to meet and use them to calculate the expected return rates. You can even use these profiles to estimate the impact of design changes to warranty returns. [Read more…]
Everything will eventually fail. I like to say that keeps us in business as reliability engineers. The trick is knowing what will fail and when. There are many potential causes for a product or system to fail. Design flaws, supplied component latent defects, assembly damage, and unexpected applied stresses exist.
The key to take away from this concept is we can learn when studying failures. We can work to prevent or delay specific types of failures. By learning about failures, we learn how to create reliable products. [Read more…]
Some of you may have heard of ‘Bayesian analysis.’ You may think this is something fancy that only universities do. Or something we never do because it is all about ‘best guesses.’ Well … neither of these things are true.
Why would you be interested in Bayesian analysis? Simple. It allows you to use EVERY PIECE OF INFORMATION you have. This can be expert judgment. Or it can be test data on a similar ‘thing.’ Or it could be combining test data for one part of your ‘thing’ combined with test data for ‘all of your thing.’ [Read more…]
In 1942, the US Air Force needed to figure out how to get more bombers home. They were losing them at an atrocious rate. They made a study of where the returning bombers were being hit. It was a great data set. But they should have done better in interpreting that data. Abraham Wald, a mathematician, corrected their assumptions and created the “Survivorship Bias principle.” This principle has changed the way we look at data sets and trends. It is a concept that I apply to field failure data sets regularly—more than once. It has caught evidence of core issues that would have otherwise been missed. [Read more…]
The toolbox for reliability engineering work is large. We have tools for risk management, data analysis, failure analysis, team building, and more. We touch on material science at the molecular level and then shift to assessing a vendor’s reliability program when you use which tool is critical to your effectiveness. So, how do you determine which tool to use for a given situation?
Let’s explore a few different situations and step through the decision process to determine which tool to employ. You may need to learn how to use a new tool or set of skills, modify how you use an existing tool, or apply what you already know how to do. [Read more…]
If reliability engineering costs you money, then you are doing it wrong. But chances are that someone you work for looks at reliability engineering as a black hole of costs. This needs to stop!
Reliability engineering is about creating value. Or money. Or more missions. Or safety. But it would help if you showed how this will happen. [Read more…]