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

ISBN-13: 9781119666288 / Angielski / Twarda / 2021 / 352 str.

Shiyu Zhou; Yong Chen

Spis treści

Chapter 1 IntroductionPart 1 Statistical Methods and Foundation for Industrial Data AnalyticsChapter 2 Introduction to Data Visualization andChapteraracterizationChapter 3 Random Vectors and the Multivariate Normal DistributionChapter 4 Explaining Covariance Structure: Principal ComponentsChapter 5 Linear Model for Numerical and CategoricalChapter 6 Linear Mixed Effects ModelPart 2 Random Effects Approaches for Diagnosis and PrognosisChapter 7 Diagnosis of Variation Source Using PCAChapter 8 Diagnosis of Variation Sources Through Random Effects EstimationChapter 9 Analysis of System DiagnosabilityChapter 10 Prognosis Through Mixed Effects Models for Longitudinal DataChapter 11 Prognosis Using Gaussian Process ModelChapter 12 Prognosis Through Mixed Effects Models for Time-to-Event DataAppendix: Basics of Vectors, Matrices, and Linear Vector SpaceReferencesIndex

O autorze

Shiyu Zhou, is a Vilas Distinguished Achievement Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. He received his doctorate in Mechanical Engineering from the University of Michigan in 2000.Yong Chen, is Professor in the Department of Industrial and Systems Engineering at the University of Iowa. He obtained his doctorate in Industrial and Operations Engineering from the University of Michigan in 2003.