Industrial Data Analytics for Diagnosis and Prognosis: A Random Effects Modelling Approach
This bookintroduces the random effects modeling approach for industrial system diagnosis and prognosis. This book contains three main parts. The first part is theory preparation, which covers general statistical concepts and theory useful for describing and modelling fixed effects, random effects, and variation in a univariate or multivariate dataset, which provides necessary background for later chapters. The next part of the book focuses on Diagnosis methods, which covers the applications of random effects approach to diagnosis of variation sources in industrial processes. A detailed performance comparison of different methods for practical applications is presented, as well. Finally, the last part of the book is on Prognosis methods. This part of the book covers the applications of random effects approach to failure prognosis in industrial processes/systems. The advanced joint prognosis model, which integrates the survival regression model and the mixed effects regression model, is also presented. The authors provide R code for all the major examples, enabling quick implementation of the techniques covered in the book.
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