Expert Knowledge Based Reliability Models: Theory and Case Study
Integrating Data and Expert Opinion Using Bayesian Statistics to Build Complex Reliability Models
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Recently there has been an increasing interest in what is called Evidence Based Asset Management. The principle of EBAM is to use all available information in form of statistical data or expert knowledge to frame the problem as a mathematical model which can be solved by optimization techniques. As we employ complex reliability and maintenance models it becomes difficult to find the necessary statistical data in appropriate formats. Available knowledge elicitation techniques usually require high proficiency in statistical and cognitive techniques. In this book after a comprehensive review of t...
Recently there has been an increasing interest in what is called Evidence Based Asset Management. The principle of EBAM is to use all available information in form of statistical data or expert knowledge to frame the problem as a mathematical model which can be solved by optimization techniques. As we employ complex reliability and maintenance models it becomes difficult to find the necessary statistical data in appropriate formats. Available knowledge elicitation techniques usually require high proficiency in statistical and cognitive techniques. In this book after a comprehensive review of the literature on knowledge elicitation and formulation techniques, I have presented a simple methodology that elicits expert knowledge to be used in different reliability models. The models can be updated later with statistical data by applying Bayesian statistics. The theory is complemented by a real industrial case study. Although the case study is based on a rather complex reliability model, the technique can easily be employed in more simple situations such as for Weibull distribution. The book is intended for engineers, consultants, and students in the field of asset management.
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