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.
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…]
Many companies haven’t the resources to purchase much more expensive software specifically for reliability, but many have the quality software Minitab. Minitab has many reliability functions available; this presentation covers the basics, beginning with “individual distribution identification” to ensure your data fits one of the reliability models. Data is then analyzed with parametric distributions (right censoring vs arbitrary censoring). Additionally, the accelerated life testing functions are explored, focusing on interpreting the results. This is presented with practical examples for clarity. [Read more…]
While the NoMTBF movement is progressing, we still find those who want to use MTBF (or MTTF). As you know, MTxx-type metrics are means. They contain little information and are generally misunderstood and misused. So, what should a well-educated practitioner do? [Read more…]
Have you ever wondered if your servicing intervals are ‘good’? Do you know what ‘good’ is? Are you servicing too much or too little? What happens if you service too much? Do you use some basic equations in textbooks and plug in numbers that seem too simple and basic, AND you don’t understand them (all at the same time)?
If you have asked yourselves any of these questions, then this is the webinar for you. A really simple, never done this before. Please join me in the discussion on what it actually means to optimize servicing intervals. [Read more…]
I recently received a question about the differences and similarities and which discipline is better concerning reliability and quality engineering. Some say reliability is a characteristic of quality, while I say reliability is quality yet over time. [Read more…]
Ok. They say there are lies, damn lies, and statistics. At the heart of statistics are these intimidating functions like ‘probability density functions’ and ‘cumulative distribution functions.’
They mean something – but are practically useless unless they help inform a decision. And we, as engineers, are supposed to know what these functions mean. But have you ever sat in a meeting or presentation where you realized some gaps in your knowledge? … or perhaps you felt like you were given too many charts but not enough help to make your decision?
If you want a really easy introduction or review of these functions – then check out this webinar!
[Read more…]