Supervision, health-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles, in order to improve reliability, availability, maintenance and life-time. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity.

This book gives an introduction into the field of fault detection, fault diagnosis and fault-tolerant systems with methods which have proven their performance in practical applications. It guides the reader in a structured tutorial style:

supervision methods, reliability, safety, system integrity and related terminology;
fault detection with signal-based methods for periodic and stochastic signals;
fault detection with process model-based methods like parameter estimation, state estimation, parity equations and principal component analysis;
fault diagnosis with classification and inference methods;
fault-tolerant systems with hardware and analytical redundancy;
many practical simulation examples and experimental results for processes like electrical motors, pumps, actuators, sensors and automotive components;
end-of-chapter exercises for self testing or for practice.

The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for practising engineers.

Klappentext

With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role.

The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems.

Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.

Inhalt

Fundamentals.- Supervision and fault management of processes - tasks and terminology.- Reliability, Availability and Maintainability (RAM).- Safety, Dependability and System Integrity.- Fault-Detection Methods.- Process Models and Fault Modelling.- Signal models.- Fault detection with limit checking.- Fault detection with signal models.- Fault detection with process-identification methods.- Fault detection with parity equations.- Fault detection with state observers and state estimation.- Fault detection of control loops.- Fault detection with Principal Component Analysis (PCA).- Comparison and combination of fault-detection methods.- Fault-Diagnosis Methods.- Diagnosis procedures and problems.- Fault diagnosis with classification methods.- Fault diagnosis with inference methods.- Fault-Tolerant Systems.- Fault-tolerant design.- Fault-tolerant components and control.- Application Examples.- Fault detection and diagnosis of DC motor drives.- Fault detection and diagnosis of a centrifugal pump-pipe-system.- Fault detection and diagnosis of an automotive suspension and the tire pressures.