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|a Wang, Jing.
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|a Data-Driven Fault Detection and Reasoning for Industrial Monitoring.
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|a 1st ed.
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|a Singapore :
|b Springer,
|c 2022.
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|c Ã2022.
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|a 1 online resource (277 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Intelligent Control and Learning Systems Series ;
|v v.3
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|a Intro -- Preface -- Contents -- Abbreviations -- 1 Background -- 1.1 Introduction -- 1.1.1 Process Monitoring Method -- 1.1.2 Statistical Process Monitoring -- 1.2 Fault Detection Index -- 1.2.1 T2 Statistic -- 1.2.2 Squared Prediction Error -- 1.2.3 Mahalanobis Distance -- 1.2.4 Combined Indices -- 1.2.5 Control Limits in Non-Gaussian Distribution -- References -- 2 Multivariate Statistics in Single Observation Space -- 2.1 Principal Component Analysis -- 2.1.1 Mathematical Principle of PCA -- 2.1.2 PCA Component Extraction Algorithm -- 2.1.3 PCA Base Fault Detection -- 2.2 Fisher Discriminant Analysis -- 2.2.1 Principle of FDA -- 2.2.2 Comparison of FDA and PCA -- References -- 3 Multivariate Statistics Between Two-Observation Spaces -- 3.1 Canonical Correlation Analysis -- 3.1.1 Mathematical Principle of CCA -- 3.1.2 Eigenvalue Decomposition of CCA Algorithm -- 3.1.3 SVD Solution of CCA Algorithm -- 3.1.4 CCA-Based Fault Detection -- 3.2 Partial Least Squares -- 3.2.1 Fundamental of PLS -- 3.2.2 PLS Algorithm -- 3.2.3 Cross-Validation Test -- References -- 4 Simulation Platform for Fault Diagnosis -- 4.1 Tennessee Eastman Process -- 4.2 Fed-Batch Penicillin Fermentation Process -- 4.3 Fault Detection Based on PCA, CCA, and PLS -- 4.4 Fault Classification Based on FDA -- 4.5 Conclusions -- References -- 5 Soft-Transition Sub-PCA Monitoring of Batch Processes -- 5.1 What Is Phase-Based Sub-PCA -- 5.2 SVDD-Based Soft-Transition Sub-PCA -- 5.2.1 Rough Stage-Division Based on Extended Loading Matrix -- 5.2.2 Detailed Stage-Division Based on SVDD -- 5.2.3 PCA Modeling for Transition Stage -- 5.2.4 Monitoring Procedure of Soft-Transition Sub-PCA -- 5.3 Case Study -- 5.3.1 Stage Identification and Modeling -- 5.3.2 Monitoring of Normal Batch -- 5.3.3 Monitoring of Fault Batch -- 5.4 Conclusions -- References.
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|a 6 Statistics Decomposition and Monitoring in Original Variable Space -- 6.1 Two Statistics Decomposition -- 6.1.1 T2 Statistic Decomposition -- 6.1.2 SPE Statistic Decomposition -- 6.1.3 Fault Diagnosis in Original Variable Space -- 6.2 Combined Index-Based Fault Diagnosis -- 6.2.1 Combined Index Design -- 6.2.2 Control Limit of Combined Index -- 6.3 Case Study -- 6.3.1 Variable Monitoring via Two Statistics Decomposition -- 6.3.2 Combined Index-Based Monitoring -- 6.3.3 Comparative Analysis -- 6.4 Conclusions -- References -- 7 Kernel Fisher Envelope Surface for Pattern Recognition -- 7.1 Process Monitoring Based on Kernel Fisher Envelope Analysis -- 7.1.1 Kernel Fisher Envelope Surface -- 7.1.2 Detection Indicator -- 7.1.3 KFES-PCA-Based Synthetic Diagnosis in Batch Process -- 7.2 Simulation Experiment Based on KFES-PCA -- 7.2.1 Diagnostic Effect on Existing Fault Types -- 7.2.2 Diagnostic Effect on Unknown Fault Types -- 7.3 Conclusions -- References -- 8 Fault Identification Based on Local Feature Correlation -- 8.1 Fault Identification Based on Kernel Discriminant Exponent Analysis -- 8.1.1 Methodology of KEDA -- 8.1.2 Simulation Experiment -- 8.2 Fault Identification Based on LLE and EDA -- 8.2.1 Local Linear Exponential Discriminant Analysis -- 8.2.2 Neighborhood-Preserving Embedding Discriminant Analysis -- 8.2.3 Fault Identification Based on LLEDA and NPEDA -- 8.2.4 Simulation Experiment -- 8.3 Cluster-LLEDA-Based Hybrid Fault Monitoring -- 8.3.1 Hybrid Monitoring Strategy -- 8.3.2 Simulation Study -- 8.4 Conclusion -- Reference -- 9 Global Plus Local Projection to Latent Structures -- 9.1 Fusion Motivation of Global Structure and Local Structure -- 9.2 Mathematical Description of Dimensionality Reduction -- 9.2.1 PLS Optimization Objective -- 9.2.2 LPP and PCA Optimization Objectives -- 9.3 Introduction to the GLPLS.
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|a 9.4 Basic Principles of GPLPLS -- 9.4.1 The GPLPLS Model -- 9.4.2 Relationship Between GPLPLS Models -- 9.4.3 Principal Components of the GPLPLS Model -- 9.5 GPLPLS-Based Quality Monitoring -- 9.5.1 Process and Quality Monitoring Based on GPLPLS -- 9.5.2 Posterior Monitoring and Evaluation -- 9.6 TE Process Simulation Analysis -- 9.6.1 Model and Discussion -- 9.6.2 Fault Diagnosis Analysis -- 9.6.3 Comparison of Different GPLPLS Models -- 9.7 Conclusions -- References -- 10 Locality-Preserving Partial Least Squares Regression -- 10.1 The Relationship Among PCA, PLS, and LPP -- 10.2 LPPLS Models and LPPLS-Based Fault Detection -- 10.2.1 The LPPLS Models -- 10.2.2 LPPLS for Process and Quality Monitoring -- 10.2.3 Locality-Preserving Capacity Analysis -- 10.3 Case Study -- 10.3.1 PLS, GLPLS and LPPLS Models -- 10.3.2 Quality Monitoring Analysis -- 10.4 Conclusions -- References -- 11 Locally Linear Embedding Orthogonal Projection to Latent Structure -- 11.1 Comparison of GPLPLS, LPPLS, and LLEPLS -- 11.2 A Brief Review of the LLE Method -- 11.3 LLEPLS Models and LLEPLS-Based Fault Detection -- 11.3.1 LLEPLS Models -- 11.3.2 LLEPLS for Process and Quality Monitoring -- 11.4 LLEOPLS Models and LLEOPLS-Based Fault Detection -- 11.5 Case Study -- 11.5.1 Models and Discussion -- 11.5.2 Fault Detection Analysis -- 11.6 Conclusions -- References -- 12 New Robust Projection to Latent Structure -- 12.1 Motivation of Robust L1-PLS -- 12.2 Introduction to RSPCA Method -- 12.3 Basic Principle of L1-PLS -- 12.4 L1-PLS-Based Process Monitoring -- 12.5 TE Simulation Analysis -- 12.5.1 Robustness of Principal Components -- 12.5.2 Robustness of Prediction and Monitoring Performance -- 12.6 Conclusions -- References -- 13 Bayesian Causal Network for Discrete Variables -- 13.1 Construction of Bayesian Causal Network -- 13.1.1 Description of Bayesian Network.
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|a 13.1.2 Establishing Multivariate Causal Structure -- 13.1.3 Network Parameter Learning -- 13.2 BCN-Based Fault Detection and Inference -- 13.3 Case Study -- 13.3.1 Public Data Sets Experiment -- 13.3.2 TE Process Experiment -- 13.4 Conclusions -- References -- 14 Probabilistic Graphical Model for Continuous Variables -- 14.1 Construction of Probabilistic Graphical Model -- 14.1.1 Multivariate Casual Structure Learning -- 14.1.2 Probability Density Estimation -- 14.1.3 Evaluation Index of Estimation Quality -- 14.2 Dynamic Threshold for the Fault Detection -- 14.3 Forward Fault Diagnosis and Reverse Reasoning -- 14.4 Case Study: Application to TEP -- 14.5 Conclusions -- References.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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4 |
|a Electronic books.
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| 700 |
1 |
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|a Zhou, Jinglin.
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| 700 |
1 |
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|a Chen, Xiaolu.
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| 776 |
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|i Print version:
|a Wang, Jing
|t Data-Driven Fault Detection and Reasoning for Industrial Monitoring
|d Singapore : Springer,c2022
|z 9789811680434
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| 797 |
2 |
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|a ProQuest (Firm)
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| 830 |
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0 |
|a Intelligent Control and Learning Systems Series
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6840160
|z Click to View
|
