Industrial Data Analytics for Diagnosis and Prognosis: A Random Effects Modelling Approach (Hardcover)

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