Industrial data analytics for diagnosis and prognosis : a random effects modelling approach / / Shiyu Zhou, Yong Chen

• Autore: Zhou Shiyu <1970->
• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021]; ©2021
• Descrizione fisica: 1 online resource (353 pages)
• Disciplina: 658.00727
• Soggetto topico: Random data (Statistics); Industrial management - Mathematics; Industrial engineering - Statistical methods
• Persona (resp. second.): ChenYong
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Industrial Data Analytics for Diagnosis and Prognosis -- Contents -- Preface -- Acknowledgments -- Acronyms -- Table of Notation -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Scope and Organization of the Book -- 1.3 How to Use This Book -- Bibliographic Note -- Part 1 Statistical Methods and Foundation for Industrial Data Analytics -- 2 Introduction to Data Visualization and Characterization -- 2.1 Data Visualization -- 2.1.1 Distribution Plots for a Single Variable -- 2.1.2 Plots for Relationship Between Two Variables -- 2.1.3 Plots for More than Two Variables -- 2.2 Summary Statistics -- 2.2.1 Sample Mean, Variance, and Covariance -- 2.2.2 Sample Mean Vector and Sample Covariance Matrix -- 2.2.3 Linear Combination of Variables -- Bibliographic Notes -- Exercises -- 3 Random Vectors and the Multivariate Normal Distribution -- 3.1 Random Vectors -- 3.2 Density Function and Properties of Multivariate Normal Distribution -- 3.3 Maximum Likelihood Estimation for Multivariate Normal Distribution -- 3.4 Hypothesis Testing on Mean Vectors -- 3.5 Bayesian Inference for Normal Distribution -- Bibliographic Notes -- Exercises -- 4 Explaining Covariance Structure: Principal Components -- 4.1 Introduction to Principal Component Analysis -- 4.1.1 Principal Components for More Than Two Variables -- 4.1.2 PCA with Data Normalization -- 4.1.3 Visualization of Principal Components -- 4.1.4 Number of Principal Components to Retain -- 4.2 Mathematical Formulation of Principal Components -- 4.2.1 Proportion of Variance Explained -- 4.2.2 Principal Components Obtained from the Correlation Matrix -- 4.3 Geometric Interpretation of Principal Components -- 4.3.1 Interpretation Based on Rotation -- 4.3.2 Interpretation Based on Low-Dimensional Approximation -- Bibliographic Notes -- Exercises. ; 5 Linear Model for Numerical and Categorical Response Variables -- 5.1 Numerical Response - Linear Regression Models -- 5.1.1 General Formulation of Linear Regression Model -- 5.1.2 Significance and Interpretation of Regression Coefficients -- 5.1.3 Other Types of Predictors in Linear Models -- 5.2 Estimation and Inferences of Model Parameters for Linear Regression -- 5.2.1 Least Squares Estimation -- 5.2.2 Maximum Likelihood Estimation -- 5.2.3 Variable Selection in Linear Regression -- 5.2.4 Hypothesis Testing -- 5.3 Categorical Response - Logistic Regression Model -- 5.3.1 General Formulation of Logistic Regression Model -- 5.3.2 Significance and Interpretation of Model Coefficients -- 5.3.3 Maximum Likelihood Estimation for Logistic Regression -- Bibliographic Notes -- Exercises -- 6 Linear Mixed Effects Model -- 6.1 Model Structure -- 6.2 Parameter Estimation for LME Model -- 6.2.1 Maximum Likelihood Estimation Method -- 6.2.2 Distribution-Free Estimation Methods -- 6.3 Hypothesis Testing -- 6.3.1 Testing for Fixed Effects -- 6.3.2 Testing for Variance-Covariance Parameters -- Bibliographic Notes -- Exercises -- Part 2 Random Effects Approaches for Diagnosis and Prognosis -- 7 Diagnosis of Variation Source Using PCA -- 7.1 Linking Variation Sources to PCA -- 7.2 Diagnosis of Single Variation Source -- 7.3 Diagnosis of Multiple Variation Sources -- 7.4 Data Driven Method for Diagnosing Variation Sources -- Bibliographic Notes -- Exercises -- 8 Diagnosis of Variation Sources Through Random Effects Estimation -- 8.1 Estimation of Variance Components -- 8.2 Properties of Variation Source Estimators -- 8.3 Performance Comparison of Variance Component Estimators -- Bibliographic Notes -- Exercises -- 9 Analysis of System Diagnosability -- 9.1 Diagnosability of Linear Mixed Effects Model -- 9.2 Minimal Diagnosable Class. ; 9.3 Measurement System Evaluation Based on System Diagnosability -- Bibliographic Notes -- Exercises -- Appendix -- 10 Prognosis Through Mixed Effects Models for Longitudinal Data -- 10.1 Mixed Effects Model for Longitudinal Data -- 10.2 Random Effects Estimation and Prediction for an Individual Unit -- 10.3 Estimation of Time-to-Failure Distribution -- 10.4 Mixed Effects Model with Mixture Prior Distribution -- 10.4.1 Mixture Distribution -- 10.4.2 Mixed Effects Model with Mixture Prior for Longitudinal Data -- 10.5 Recursive Estimation of Random Effects Using Kalman Filter -- 10.5.1 Introduction to the Kalman Filter -- 10.5.2 Random Effects Estimation Using the Kalman Filter -- Biographical Notes -- Exercises -- Appendix -- 11 Prognosis Using Gaussian Process Model -- 11.1 Introduction to Gaussian Process Model -- 11.2 GP Parameter Estimation and GP Based Prediction -- 11.3 Pairwise Gaussian Process Model -- 11.3.1 Introduction to Multi-output Gaussian Process -- 11.3.2 Pairwise GP Modeling Through Convolution Process -- 11.4 Multiple Output Gaussian Process for Multiple Signals -- 11.4.1 Model Structure -- 11.4.2 Model Parameter Estimation and Prediction -- 11.4.3 Time-to-Failure Distribution Based on GP Predictions -- Bibliographical Notes -- Exercises -- 12 Prognosis Through Mixed Effects Models for Time-to-Event Data -- 12.1 Models for Time-to-Event Data Without Covariates -- 12.1.1 Parametric Models for Time-to-Event Data -- 12.1.2 Non-parametric Models for Time-to-Event Data -- 12.2 Survival Regression Models -- 12.2.1 Cox PH Model with Fixed Covariates -- 12.2.2 Cox PH Model with Time Varying Covariates -- 12.2.3 Assessing Goodness of Fit -- 12.3 Joint Modeling of Time-to-Event Data and Longitudinal Data -- 12.3.1 Structure of Joint Model and Parameter Estimation -- 12.3.2 Online Event Prediction for a New Unit. ; 12.4 Cox PH Model with Frailty Term for Recurrent Events -- Bibliographical Notes -- Exercises -- Appendix -- Appendix: Basics of Vectors, Matrices, and Linear Vector Space -- References -- Index.
• Sommario/riassunto: "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"--
• Titolo autorizzato: Industrial data analytics for diagnosis and prognosis
• ISBN: 1-5231-4353-3; 1-119-66630-9; 1-119-66627-9; 1-119-66629-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830968103321
• Lo trovi qui: Univ. Federico II