- Chapter 1 Introduction
- Part 1 Statistical Methods and Foundation for Industrial Data Analytics
- Chapter 2 Introduction to Data Visualization andChapteraracterization
- Chapter 3 Random Vectors and the Multivariate Normal Distribution
- Chapter 4 Explaining Covariance Structure: Principal Components
- Chapter 5 Linear Model for Numerical and Categorical
- Chapter 6 Linear Mixed Effects Model
- Part 2 Random Effects Approaches for Diagnosis and Prognosis
- Chapter 7 Diagnosis of Variation Source Using PCA
- Chapter 8 Diagnosis of Variation Sources Through Random Effects Estimation
- Chapter 9 Analysis of System Diagnosability
- Chapter 10 Prognosis Through Mixed Effects Models for Longitudinal Data
- Chapter 11 Prognosis Using Gaussian Process Model
- Chapter 12 Prognosis Through Mixed Effects Models for Time-to-Event Data Appendix: Basics of Vectors, Matrices, and Linear Vector Space References Index.
- (source: Nielsen Book Data)

Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model In Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book's two parts, general statistical concepts and useful theory are described and explained, as are industrial diagnosis and prognosis methods. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover the application of the random effects approach to diagnosis of variation sources in industrial processes. They offer a detailed performance comparison of different diagnosis methods before moving on to the application of the random effects approach to failure prognosis in industrial processes and systems. In addition to presenting the joint prognosis model, which integrates the survival regression model with the mixed effects regression model, the book also offers readers: A thorough introduction to describing variation of industrial data, including univariate and multivariate random variables and probability distributions Rigorous treatments of the diagnosis of variation sources using PCA pattern matching and the random effects model An exploration of extended mixed effects model, including mixture prior and Kalman filtering approach, for real time prognosis A detailed presentation of Gaussian process model as a flexible approach for the prediction of temporal degradation signals Ideal for senior year undergraduate students and postgraduate students in industrial, manufacturing, mechanical, and electrical engineering, Industrial Data Analytics for Diagnosis and Prognosis is also an indispensable guide for researchers and engineers interested in data analytics methods for system diagnosis and prognosis. .

(source: Nielsen Book Data)