Description
Book SynopsisDiscover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model
InIndustrial Data Analytics for Diagnosis and Prognosis -A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen delivera 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 datasetsand 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 methodsbefore moving on to the application of the random effects approach to failure prognosis in indu
Table of Contents
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
