1st Edition
Filter-Based Fault Diagnosis and Remaining Useful Life Prediction
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.
Chapter 1. Introduction
1.1 Introduction
1.2 Fault Diagnosis
1.3 Remaining Useful Life Prediction
1.4 Outline of This Book
Chapter 2. Filter/Estimator Design of Networked Multirate Sampled Systems with Network-induced Phenomena
2.1 Estimator Design with Measurement Quantization and Sensor Failures
2.2 Finite-time Filter Design with Event-based Relay and Fading Channels
2.3 Conclusion
Chapter 3. Fault Detection of Networked Multirate Systems with Filter-based Methods
3.1 Fault Detection with Fading Measurements and Randomly Occurring Faults
3.2 Fault Detection with Dynamic Quantization and Intermittent Faults
3.3 Conclusion
Chapter 4. Fault Diagnosis of Multirate Time-varying Systems with Filter-based Methods
4.1 Event-based Fault Diagnosis with Constrained Fault
4.2 Event-based Fault Diagnosis with Bounded Unknown Fault
4.3 Conclusion
Chapter 5. Fault Diagnosis of Modular Multilevel Converters with Machine Learning Methods
5.1 Fault Diagnosis with Mixed Kernel Support Tensor Machine
5.2 Fault Diagnosis with Synchrosqueezing Transform and Optimized Deep CNN
5.3 Conclusion
Chapter 6. Remaining Useful Life Prediction of Industrial Components with Filterbased Methods
6.1 Remaining Useful Life Prediction with Adaptive UKF and SVR
6.2 Remaining Useful Life Prediction with ALF Optimized PF and LSTM
6.3 Remaining Useful Life Prediction with Degradation Point Detection and EKF
6.4 Conclusion
Chapter 7. Remaining Useful Life Prediction of Industrial Components with Machine Learning Methods
7.1 Remaining Useful Life Prediction with WPT and Optimized SVR
7.2 Remaining Useful Life Prediction with Complete Ensemble EMD and GRU
7.3 Remaining Useful Life Prediction with PSR and Error Compensation
7.4 Conclusion
Chapter 8. Conclusions and Future Topics
Biography
Yong Zhang
