Springer Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools (Advances in Industrial Control)

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: • new material on fault isolation and identification and alarm management; • extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; • addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and • enhanced discussion of residual evaluation which now deals with stochastic processes. Model-based Fault Diagnosis Techniques will interest academic researchers working in fault identification and diagnosis and as a text it is suitable for graduate students in a formal university-based course or as a self-study aid for practising engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering. From the Back Cover Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagnosis Techniques will interest academic researchers working in fault detection and diagnosis and as a textbook it is suitable for graduate students in a formal university-based course or as a self-study aid for practicing engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering.Model-based Fault Diagnosis Techniques will interest academic researchers working in fault detection and diagnosis and as a textbook it is suitable for graduate students in a formal university-based course or as a self-study aid for practicing engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering. ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagnosis Techniques will interest academic researchers working in fault detection and diagnosis and as a textbook it is suitable for graduate students in a formal university-based course or as a self-study aid for practicing engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control. About the Author Steven X. Ding began investigating issues concerned with