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E-grāmata: Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach: A Random Effects Modelling Approach [Wiley Online]

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  • Formāts: 352 pages
  • Izdošanas datums: 24-Aug-2021
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119666279
  • ISBN-13: 9781119666271
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  • Wiley Online
  • Cena: 137,50 €*
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Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach: A Random Effects Modelling ApproachPalielināt
  • Formāts: 352 pages
  • Izdošanas datums: 24-Aug-2021
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119666279
  • ISBN-13: 9781119666271
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119666271
"Today, we are facing a data rich world that is changing faster than ever before. The ubiquitous availability of data provides great opportunities for industrial enterprises to improve their process quality and productivity. Industrial data analytics is the process of collecting, exploring, and analyzing data generated from industrial operations and throughout the product life cycle in order to gain insights and improve decision-making. This book describes industrial data analytics approaches with an emphasis on diagnosis and prognosis of industrial processes and systems. A large number of textbooks/research monographs exist on diagnosis and prognosis in the engineering eld. Most of these engineering books focus on model-based diagnosis and prognosis problems in dynamic systems. The modelbased approaches adopt a dynamic model for the system, often in the form of a state space model, as the basis for diagnosis and prognosis. Dierent from these existing books, this book focuses on the concept of random effects and its applications in system diagnosis and prognosis. The impetus for this book arose from the current digital revolution. In this digital age, the essential feature of a modern engineering system is that a large amount of data from multiple similar units/machines during their operations are collected in real time. This feature poses signicant intellectual opportunities and challenges. As for opportunities, since we have observations from potentially a very large number of similar units, we can compare their operations, share the information, and extract common knowledge to enable accurate and tailored prediction and control at the individual level. As for challenges, because the data are collected in the field and not in a controlled environment, the data contain signicant variation and heterogeneity due to the large variations in working/usage conditions for dierent units. This requires that the analytics approaches should be not only general (so that the common information can be learned and shared), but also flexible (so that the behaviour of an individual unit can be captured and controlled). The random effects modeling approaches can exactly address these opportunities and challenges"--

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. 

Preface xi
Acknowledgments xiii
Acronyms xv
Table of Notation
xvii
1 Introduction
1(10)
1.1 Background and Motivation
1(5)
1.2 Scope and Organization of the Book
6(2)
1.3 How to Use This Book
8(3)
Bibliographic Notes
8(3)
Part 1 Statistical Methods and Foundation for Industrial Data Analytics
11(122)
2 Introduction to Data Visualization and Characterization
13(24)
2.1 Data Visualization
16(10)
2.1.1 Distribution Plots for a Single Variable
16(3)
2.1.2 Plots for Relationship Between Two Variables
19(3)
2.1.3 Plots for More than Two Variables
22(4)
2.2 Summary Statistics
26(11)
2.2.1 Sample Mean, Variance, and Covariance
26(4)
2.2.2 Sample Mean Vector and Sample Covariance Matrix
30(2)
2.2.3 Linear Combination of Variables
32(2)
Bibliographic Notes
34(1)
Exercises
34(3)
3 Random Vectors and the Multivariate Normal Distribution
37(24)
3.1 Random Vectors
37(4)
3.2 Density Function and Properties of Multivariate Normal Distribution
41(4)
3.3 Maximum Likelihood Estimation for Multivariate Normal Distribution
45(1)
3.4 Hypothesis Testing on Mean Vectors
46(5)
3.5 Bayesian Inference for Normal Distribution
51(10)
Bibliographic Notes
56(1)
Exercises
57(4)
4 Explaining Covariance Structure: Principal Components
61(20)
4.1 Introduction to Principal Component Analysis
61(9)
4.1.1 Principal Components for More Than Two Variables
64(2)
4.1.2 PC A with Data Normalization
66(1)
4.1.3 Visualization of Principal Components
67(2)
4.1.4 Number of Principal Components to Retain
69(1)
4.2 Mathematical Formulation of Principal Components
70(4)
4.2.1 Proportion of Variance Explained
71(2)
4.2.2 Principal Components Obtained from the Correlation Matrix
73(1)
4.3 Geometric Interpretation of Principal Components
74(7)
4.3.1 Interpretation Based on Rotation
74(2)
4.3.2 Interpretation Based on Low-Dimensional Approximation
76(2)
Bibliographic Notes
78(1)
Exercises
79(2)
5 Linear Model for Numerical and Categorical Response Variables
81(28)
5.1 Numerical Response -- Linear Regression Models
81(8)
5.1.1 General Formulation of Linear Regression Model
84(1)
5.1.2 Significance and Interpretation of Regression Coefficients
84(1)
5.1.3 Other Types of Predictors in Linear Models
85(4)
5.2 Estimation and Inferences of Model Parameters for Linear Regression
89(11)
5.2.1 Least Squares Estimation
90(4)
5.2.2 Maximum Likelihood Estimation
94(2)
5.2.3 Variable Selection in Linear Regression
96(1)
5.2.4 Hypothesis Testing
97(3)
5.3 Categorical Response -- Logistic Regression Model
100(9)
5.3.1 General Formulation of Logistic Regression Model
103(1)
5.3.2 Significance and Interpretation of Model Coefficients
104(1)
5.3.3 Maximum Likelihood Estimation for Logistic Regression
105(1)
Bibliographic Notes
106(1)
Exercises
106(3)
6 Linear Mixed Effects Model
109(24)
6.1 Model Structure
109(5)
6.2 Parameter Estimation for LME Model
114(10)
6.2.1 Maximum Likelihood Estimation Method
114(7)
6.2.2 Distribution-Free Estimation Methods
121(3)
6.3 Hypothesis Testing
124(9)
6.3.1 Testing for Fixed Effects
125(3)
6.3.2 Testing for Variance--Covariance Parameters
128(2)
Bibliographic Notes
130(1)
Exercises
131(2)
Part 2 Random Effects Approaches for Diagnosis and Prognosis
133(176)
7 Diagnosis of Variation Source Using PCA
135(24)
7.1 Linking Variation Sources to PCA
136(4)
7.2 Diagnosis of Single Variation Source
140(6)
7.3 Diagnosis of Multiple Variation Sources
146(6)
7.4 Data Driven Method for Diagnosing Variation Sources
152(7)
Bibliographic Notes
155(1)
Exercises
156(3)
8 Diagnosis of Variation Sources Through Random Effects Estimation
159(16)
8.1 Estimation of Variance Components
161(5)
8.2 Properties of Variation Source Estimators
166(2)
8.3 Performance Comparison of Variance Component Estimators
168(7)
Bibliographic Notes
172(1)
Exercises
172(3)
9 Analysis of System Diagnosability
175(12)
9.1 Diagnosability of Linear Mixed Effects Model
175(5)
9.2 Minimal Diagnosable Class
180(3)
9.3 Measurement System Evaluation Based on System Diagnosability
183(4)
Bibliographic Notes
184(1)
Exercises
184(2)
Appendix
186(1)
10 Prognosis Through Mixed Effects Models for Longitudinal Data
187(46)
10.1 Mixed Effects Model for Longitudinal Data
188(9)
10.2 Random Effects Estimation and Prediction for an Individual Unit
197(4)
10.3 Estimation of Time-to-Failure Distribution
201(5)
10.4 Mixed Effects Model with Mixture Prior Distribution
206(12)
10.4.1 Mixture Distribution
208(2)
10.4.2 Mixed Effects Model with Mixture Prior for Longitudinal Data
210(8)
10.5 Recursive Estimation of Random Effects Using Kalman Filter
218(15)
10.5.1 Introduction to the Kalman Filter
219(3)
10.5.2 Random Effects Estimation Using the Kalman Filter
222(3)
Biographical Notes
225(1)
Exercises
226(2)
Appendix
228(5)
11 Prognosis Using Gaussian Process Model
233(34)
11.1 Introduction to Gaussian Process Model
234(3)
11.2 GP Parameter Estimation and GP Based Prediction
237(8)
11.3 Pairwise Gaussian Process Model
245(11)
11.3.1 Introduction to Multi-output Gaussian Process
246(2)
11.3.2 Pairwise GP Modeling Through Convolution Process
248(8)
11.4 Multiple Output Gaussian Process for Multiple Signals
256(11)
11.4.1 Model Structure
256(2)
11.4.2 Model Parameter Estimation and Prediction
258(5)
11.4.3 Time-to-Failure Distribution Based on GP Predictions
263(1)
Bibliographical Notes
263(1)
Exercises
264(3)
12 Prognosis Through Mixed Effects Models for Time-to-Event Data
267(42)
12.1 Models for Time-to-Event Data Without Covariates
269(12)
12.1.1 Parametric Models for Time-to-Event Data
271(6)
12.1.2 Non-parametric Models for Time-to-Event Data
277(4)
12.2 Survival Regression Models
281(12)
12.2.1 Cox PH Model with Fixed Covariates
282(3)
12.2.2 Cox PH Model with Time Varying Covariates
285(1)
12.2.3 Assessing Goodness of Fit
286(7)
12.3 Joint Modeling of Time-to-Event Data and Longitudinal Data
293(6)
12.3.1 Structure of Joint Model and Parameter Estimation
294(3)
12.3.2 Online Event Prediction for a New Unit
297(2)
12.4 Cox PH Model with Frailty Term for Recurrent Events
299(10)
Bibliographical Notes
304(1)
Exercises
305(2)
Appendix
307(2)
Appendix: Basics of Vectors, Matrices, and Linear Vector Space 309(6)
References 315(12)
Index 327
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.
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