Your Reliability Engineering Professional Development Site
Apple recently did something that it isn’t in the habit of doing. And that is – admitting failure. After spending $ 10 billion on ‘Project Titan’ whose aim was to produce a ‘really cool car,’ Apple decided to pull the pin and cancel it.
Simplifying Apple’s Project Titan ambitions to something as pithy as creating a ‘really cool car’ might seem a little condescending. The problem for Apple was that unfortunately, this was the practical truth. And that is why it failed.
We often use the term ‘system reliability modelling’ to describe any scenario where we need to somehow convert what we know about component reliability into system reliability in order to make a decision. Some of us might have heard of ‘series systems’ that have no redundant components, meaning that the system fails when any component fails. Then there are ‘parallel systems’ where the system fails when the last component fails. But then there are ‘K out of N’ systems that need ‘K’ components out of a total of ‘N’ components to work for the system to work. Do you know when ‘K out of N’ system reliability looks like series system reliability? … or parallel system reliability? … or perhaps you don’t know what series or parallel system reliability looks like? Then this webinar is for you.
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Chris and Fred discuss the similarities and differences between two terms that many people think mean the same thing.
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Chris and Fred discuss a listener’s question about dealing with different ‘duration’ metrics for their field data … so how do we find things like ‘operating time’ and other age-related metrics that aren’t calendar time that simply aren’t there?
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We like to think we make decisions based on information. We don’t. We make decisions based on emotions. And the most important emotion we rely upon for decision-making is confidence. We can be provided all the information in the world, but if we can’t understand it, trust it or believe it, we look for confidence in other ways. Often to disastrous outcomes.
The number of ‘well-funded’ production efforts full of ‘very smart people’ that routinely generate expensive but unreliable products is sadly, very high. And it all comes down to the wrong types of confidence that well-paid decision-makers chase.
Chris and Fred discuss the importance of being helpful when it comes to reliability engineering. After all, it is much easier to have people want to come to you as opposed to forcing them to do something they don’t want to. To be influential is to be helpful.
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Chris and Fred discuss how formulae (or formulas!) can be very important … but it is way more important to understand what formulae represent, and how they work. Listen to this podcast if you want to learn more!
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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.
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Chris and Fred discuss the differences, pros and cons of FTA (fault tree analysis) and RBD (reliability block diagrams). Need to learn more about how to model system reliability? Do you struggle to spell FTA or RBDs? Listen to this podcast!
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Chris and Fred discuss about this thing called a ‘k out of n’ system … and how most people don’t really know about how they contribute to system reliability. Do you know what the reliability curve of a ‘k out of n’ system looks like when compared to series or parallel systems? Don’t know what these systems are? Listen to this podcast!
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What does it mean to be a ‘visionary leader’? It starts with being different to most people. You can’t just become a visionary leader by completing a leadership course, being mentored by someone awesome, or by compiling an impressive curriculum vitae (although these can help people with the potential to become visionary leaders get there).
One of the first things that the then United States Army Chief of Staff (General George C. Marshall) did at the outbreak of World War II was to fire the majority of his officers who had climbed the ladder of military ranks throughout the previous 20 years of relative peace.
Boeing is really having a bad stretch. Or more specifically, the passengers flying in its 737 MAX aircraft are.
Most recently a ‘plug’ flew off the side of Boeing 737 MAX 9 plane in flight, leaving a refrigerator sized hole next to startled (but mercifully still living) passengers. A ‘plug’ is a panel that seals up a hole in the fuselage that is included during manufacture to allow an optional emergency exit to be installed.
This failure is not a good look … especially for a three-month-old plane. Lots of manufacturers of different machines throughout history have been able to successfully bolt panels to cover holes of a similar size to that of an aircraft emergency exit. It is not hard to do. Nor is it hard to have systems in place to make sure it is done right.
Chris and Fred discuss some of the things we have learned in the world of reliability directly due to World War II. Not many people know that around one third of today’s medical vaccines were developed out of necessity from World War II … and something similar happened when it came to making things reliable.
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Chris and Fred discuss what it means to ‘assume’ something. We assume things all the time in our daily lives. We would never get anything done if we didn’t rely on quick ‘guesses’ about the world around us. But then there are those ‘assumptions’ that get us in trouble. So what’s the difference?
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Chris and Fred discuss when you should do this thing called Monte Carlo simulation? … in fact … what is it?
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