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E-raamat: Filter-Based Fault Diagnosis and Remaining Useful Life Prediction [Taylor & Francis e-raamat]

(Wuhan Univ. of Science & Tech., China), (Brunel Uni, UK), (Huazhong Univ. of Science & Tech., China)
  • Formaat: 262 pages, 39 Tables, black and white; 100 Line drawings, black and white; 10 Halftones, black and white; 110 Illustrations, black and white
  • Ilmumisaeg: 10-Feb-2023
  • Kirjastus: CRC Press
  • ISBN-13: 9781003330998
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 193,88 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 276,97 €
  • Säästad 30%
Filter-Based Fault Diagnosis and Remaining Useful Life PredictionSuurem pilt
  • Formaat: 262 pages, 39 Tables, black and white; 100 Line drawings, black and white; 10 Halftones, black and white; 110 Illustrations, black and white
  • Ilmumisaeg: 10-Feb-2023
  • Kirjastus: CRC Press
  • ISBN-13: 9781003330998
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003330998
This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning.

Features:











Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities.





Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective.





Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system.





Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems.





Includes simulation examples in each chapter to reflect the engineering practice.

This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.
Preface xi
Acknowledgements xiii
Author Biographies xv
List of Figures
xix
List of Tables
xxv
Symbols xxvii
1 Introduction
1(12)
1.1 Introduction
1(1)
1.2 Fault Diagnosis
1(6)
1.2.1 Filter-Based Fault Diagnosis
1(5)
1.2.2 Data-Driven Fault Diagnosis
6(1)
1.3 Remaining Useful Life Prediction
7(3)
1.3.1 Data-Driven Remaining Useful Life Prediction
9(1)
1.3.2 Filter-Based Remaining Useful Life Prediction
9(1)
1.4 Outline of This Book
10(3)
2 Filter/Estimator Design of Networked Multi-rate Sampled Systems with Network-Induced Phenomena
13(38)
2.1 Estimator Design with Measurement Quantization and Sensor Failures
14(15)
2.1.1 Problem Formulation
14(5)
2.1.2 Variance-Constrained Estimator Design
19(8)
2.1.3 Illustrative Examples
27(2)
2.2 Finite-Time Filter Design with Event-Based Relay and Fading Channels
29(21)
2.2.1 Problem Formulation
30(5)
2.2.2 Finite-Time Filter Design
35(9)
2.2.3 Illustrative Examples
44(6)
2.3 Conclusion
50(1)
3 Fault Detection of Networked Multi-rate Systems with Filter-Based Methods
51(34)
3.1 Fault Detection with Fading Measurements and Randomly Occurring Faults
52(12)
3.1.1 Problem Formulation
52(4)
3.1.2 Detection of Randomly Occurring Faults
56(4)
3.1.3 Illustrative Examples
60(4)
3.2 Fault Detection with Dynamic Quantization and Intermittent Faults
64(18)
3.2.1 Problem Formulation
64(5)
3.2.2 Detection of Intermittent Faults
69(8)
3.2.3 Illustrative Example
77(5)
3.3 Conclusion
82(3)
4 Fault Diagnosis of Multi-rate Time-Varying Systems with Filter-Based Methods
85(40)
4.1 Event-Based Fault Diagnosis with Constrained Fault
86(16)
4.1.1 Problem Formulation
86(1)
4.1.2 Fault Detection and Fault Isolation
87(11)
4.1.3 Illustrative Examples
98(4)
4.2 Event-Based Fault Diagnosis with Bounded Unknown Fault
102(20)
4.2.1 Problem Formulation
104(1)
4.2.2 Fault Diagnosis and Fault Estimation
105(11)
4.2.3 Illustrative Examples
116(6)
4.3 Conclusion
122(3)
5 Fault Diagnosis of Modular Multilevel Converters with Machine Learning Methods
125(28)
5.1 Fault Diagnosis with Mixed Kernel Support Tensor Machine
126(13)
5.1.1 Operating Principles of Modular Multilevel Converters
126(1)
5.1.2 Mixed Kernel Support Tensor Machine
127(4)
5.1.3 Fault Diagnosis
131(1)
5.1.4 Illustrative Examples
132(7)
5.2 Fault Diagnosis with Synchrosqueezing Transform and Optimized Deep CNN
139(12)
5.2.1 Synchrosqueezing Transform
139(1)
5.2.2 Optimized Deep Convolutional Neural Network
140(2)
5.2.3 Fault Diagnosis
142(1)
5.2.4 Illustrative Examples
143(8)
5.3 Conclusion
151(2)
6 Remaining Useful Life Prediction of Industrial Components with Filter-Based Methods
153(42)
6.1 Remaining Useful Life Prediction with Adaptive UKF and SVR
154(11)
6.1.1 Genetic Algorithm Optimized Support Vector Regression
157(1)
6.1.2 Remaining Useful Life Prediction of Lithium-Ion Batteries
158(1)
6.1.3 Illustrative Examples
159(6)
6.2 Remaining Useful Life Prediction with ALF-Optimized PF and LSTM
165(10)
6.2.1 Adaptive Levy Flight Optimized Particle Filter
166(3)
6.2.2 Remaining Useful Life Prediction of Lithium-Ion Batteries
169(1)
6.2.3 Illustrative Examples
170(5)
6.3 Remaining Useful Life Prediction with Degradation Point Detection and EKF
175(19)
6.3.1 Degradation Point Detection
179(3)
6.3.2 Health Indicator Construction
182(1)
6.3.3 Remaining Useful Life Prediction of Bearings
183(2)
6.3.4 Illustrative Examples
185(9)
6.4 Conclusion
194(1)
7 Remaining Useful Life Prediction of Industrial Components with Machine Learning Methods
195(38)
7.1 Remaining Useful Life Prediction with WPT and Optimized SVR
196(12)
7.1.1 Degenerate Poi nt Detection
196(2)
7.1.2 Remaining Useful Life Prediction of Turbine Engines
198(3)
7.1.3 Illustrative Examples
201(7)
7.2 Remaining Useful Life Prediction with Complete Ensemble EMD andGRU
208(11)
7.2.1 Health Indicator Construction
208(4)
7.2.2 Remaining Useful Life Prediction of Bearings
212(3)
7.2.3 Illustrative Examples
215(4)
7.3 Remaining Useful Life Prediction with PSR and Error Compensation
219(12)
7.3.1 Health Indicator Construction
220(1)
7.3.2 Remaining Useful Life Prediction of Lithium-Ion Batteries
221(5)
7.3.3 Illustrative Examples
226(5)
7.4 Conclusion
231(2)
8 Conclusions and Future Topics
233(2)
Bibliography 235(26)
Index 261
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