Your Reliability Engineering Professional Development Site
Chris and Fred discuss what came first … reliability or statistics? Reliability of course! There are plenty of organizations that focus on baking reliability into designs without the need for statistics. But statistics are a part of reliability engineering. So what is the relationship between the two? Listen to this podcast to learn more.
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Andre and Fred discussing the benefits of using what you know as you approach setting sample sizes using Bayesian statistics.
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James and Fred discussing a better way to set stocking levels of spares.
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James and Fred discussing dealing with the data we gather.
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In today’s episode, the guest Fred Schenkelberg explains the Monte Carlo simulation in a fair amount of detail. Before you get in the depth of how the tool works, you need to understand what basically this method is. The Monte Carlo simulation has been around since World War 2 and it is a mathematical technique that works based on probability functions, random variables, and the distribution of statistical data. The main concept of it to give the decision maker the most obvious choices while facing any risks to get the best out of every possible outcome. The tool serves the purpose for getting a better insight of the consequences relative to each choice the person making decisions has to make. When you’re looking for the reliability of your assets and checking the integrity of your different maintenance programs, it is a really powerful tool.
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Andre and Fred discussing the language of variation and why you need to speak this language as a reliability engineer.
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Adam and Fred discussing the role of statistical tools in everyday reliability engineering.
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How do you use the Weibull Distribution?
It’s just one of many useful statistical distributions we have to master as reliability engineers.
Let’s explore an array of distribution and the problems they can help solve in our day to day work.
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Mastering the statistical tools related to reliability engineering allows you to master reliability.
Identify, characterize, understand, predict, and improve reliability all require statistics. Let’s discuss how it works and will work for you.
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