Filter-Based Fault Diagnosis and Remaining Useful Life Prediction

1. Auflage 2024
ISBN: 978-1-032-36254-0
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 290 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 408 g

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.

Zielgruppe

Academic

Autoren/Hrsg.

Zhang, Yong

Wang, Zidong

Yuan, Ye

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

1. Introduction. 2. Filter/Estimator Design of Networked Multirate Sampled Systems with Network-induced Phenomena. 3. Fault Detection of Networked Multirate Systems with Filter-based Methods. 4. Fault Diagnosis of Multirate Time-varying Systems with Filter-based Methods. 5. Fault Diagnosis of Modular Multilevel Converters with Machine Learning Methods. 6. Remaining Useful Life Prediction of Industrial Components with Filterbased Methods. 7. Remaining Useful Life Prediction of Industrial Components with Machine Learning Methods. 8. Conclusions and Future Topics.