Model-based fault diagnosis: methods for state-space systems

by Wang, Zhenhua

Type Book

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Title Statements

Full title :Model-based fault diagnosis : methods for state-space systems / Zhenhua Wang, Yi Shen.

Summary

This book investigates in detail model-based fault diagnosis methods, including observer-based residual generation, residual evaluation based on threshold computation, observer-based fault isolation strategies, observer-based fault estimation, Kalman filter-based fault diagnosis methods, and parity space approach. Studies on model-based fault diagnosis have attracted engineers and scientists from various disciplines, such as electrical, aerospace, mechanical, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of state-space approach. The methods introduced in the book are systemic and easy to follow. The book is intended for undergraduate and graduate students who are interested in fault diagnosis and state estimation, researchers investigating fault diagnosis and fault-tolerant control, and control system design engineers working on safety-critical systems.

Edition Statements

Publication Date:2023

Publication Statements

Publisher(s):Springer
Place of Publication:Singapore
Language:eng
Publication Date:2023

The Physical Object

Description:1 online resource (xv, 200 pages) : : illustrations (some color).

Edition Identifiers

ISBN:9789811967061, 9811967067
DOI:10.1007/978-981-19-6706-1
oclc:on1349352138
LC Classification:TA169.6
Dewey Decimal:620/.0044

Introduction -- Modelling for Fault Diagnosis -- Observer-Based Residual Generation -- Robust Residual Generation Based on Unknown Input Observer -- Optimal Robust Residual Generation -- Residual Evaluation Based on Threshold Computation -- Fault Isolation Based on Structured Residual Generation -- Fault Isolation Based on Directional Residual Generation -- Observer-Based Fault Estimation -- Kalman Filter-Based Fault Diagnosis -- Parity Space Approaches.