Your Reliability Engineering Professional Development Site
In this article series, we will review stories and examples to highlight the importance of product field data and how it can be used to drive business decisions. We will learn the benefits and when to use different analyses to detect issues using data exploration tools such as histograms, box plots, life distribution, and Weibull analysis. We will also cover statistical techniques for root cause investigation, such as Regression Analysis and Decision Tree. Fundamentals of reliability modeling techniques such as Reliability Block Diagrams, Fault Tree Analysis, and Monte Carlo Analysis will also be covered.
Normal distribution is most common in real life scenarios be it modeling any reliability performance parameter at a specific time. Central Limit Theorem (CLT) shows why normal distribution occurs so often.
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Having a knowledge of how the data is distributed is critical to model failure times and life in reliability analysis. Every distribution is unique and suitable for different types of reliability data.
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In the current century, data is ubiquitous but how to make use of it to drive product decisions can take time and effort. Data visualization is a useful technique to identify patterns in a dataset and derive exploratory statistics to analyze trends.
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