An Accendo Reliability recorded webinar event

Sometimes the equations we need to model reliability are just so complicated that we simply avoid them, or use other equations that are simpler but model the wrong thing (see some of my earlier webinars!). But it doesn’t have to be this way. Monte Carlo simulation is so simple that anyone who has access to Microsoft Excel can use it. What this means is that we don’t need to use complicated equations (like those use to model things like switching systems) and instead can use Microsoft Excel to help us get approximate (but really accurate) answers. Sound interesting? Join us for this webinar!

An Accendo Reliability recorded webinar event

One role reliability engineers enjoy is that of analyzing data. We have available to us a wide range of statistical methods, yet each does require some form of data. In some cases, we are data rich with almost too much data. In other situations, we are lacking data or have insufficient data.

An Accendo Reliability recorded webinar event

Some of us might have heard of the Weibull distribution. Some of us might have heard about Weibull analysis. Weibull analysis uses the Weibull distribution to help us visualize failure data in a really useful way. We can ‘see’ if our product is wearing out. We can see if our product is wearing in. We can estimate how many things will have failed by certain times. We can do all manners of other wonderful ‘reliability engineering’ things through Weibull Analysis. But then there is ‘WeiBayes Analysis.’ It combines Weibull and Bayesian analysis (… I know that doesn’t mean much to most people). It can be really useful if we know ‘how’ something we are testing fails. Because if we do, then we can essentially feed a certain parameter into the analysis to help it on its way. This means that we don’t need as much data (always a good thing in reliability engineering). But there are quite a few catches. Interested in learning about Weibayes analysis? Join us for this webinar.

An Accendo Reliability recorded webinar event

Some of you might have heard about probability plots … like Weibull plots. Some of you might not. A Weibull plot is a really useful way of quickly ‘looking’ at data and being able to ‘see’ really useful things. This could be seeing that a system’s failure rate is decreasing over time … which usually means there are manufacturing defects. It could be seeing that the rate of failure of a system is increasing over time … which means the system is accumulating damage or wearing out. And lots of other things. So, let’s look at a different sort of ‘probability’ plot … and see what we can work out from it. And that means working out what we need to do to improve reliability. Join this webinar if you want to learn how to see really useful things in lines that you might not have appreciated in the past.

An Accendo Reliability recorded webinar event

Most humans can be quite technically minded. And sometimes we aren’t. Sometimes, we expect every drug or medical procedure to be checked, approved, validated, and tested on other people (et cetera) before subjecting ourselves to whatever our doctor suggests. At other times, we spend lots of money at casinos (note that I said ‘spend’ and not ‘invest’ or ‘win’). When it comes to reliability, we can sometimes be too technically minded. A reliability ‘number’ might not exist until our product has undergone exhaustive testing. Which can often be two years too late to do anything about if it turns out it doesn’t meet all our reliability dreams. So what can we do? Well … why spend money trying to generate information (through lengthy tests) when you can use the information stored in everyone’s brain? The most common answer (even if we don’t want to admit it) is that this sounds like guesswork. And guesswork can sound unprofessional. Or it’s just wrong. But there are ways you can suck out information from a group of experts in a quantifiable and remarkably accurate way. Want to learn more? This webinar introduces you to some of the concepts that might interest you.

An Accendo Reliability recorded webinar event

What role does ‘wishful thinking’ play in interpreting reliability test results? Hopefully, none. A well-designed test will likely provide meaningful results. You and your team may not like the results, yet the results will be clear.

An Accendo Reliability recorded webinar event

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.

An Accendo Reliability recorded webinar event

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.

An Accendo Reliability recorded webinar 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.

An Accendo Reliability recorded webinar event

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.’

An Accendo Reliability recorded webinar event

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.

An Accendo Reliability recorded webinar event

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.

An Accendo Reliability recorded webinar event

If you are a reliability engineer – chances are you have done a Weibull plot. You take something like failure data, put it into a piece of software, and presto!