With increasing demands for efficiency and product quality and
progressing 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, the reliability, availability, safety and systems integrity
of technical processes is considered. Then fault-detection methods
for single signals without models like limit and trend checking and
with harmonic and stochastic models, like Fourier analysis,
correlation and wavelets are treated. This is followed by fault
detection with process models using the relationships between
signals like 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.
Especially for safety-critical processes fault-tolerant systems
are required. Basic redundant structures like n-out-of-m systems,
cold and hot standby are considered and ways to design
fault-tolerant sensors, actuators and control systems are
outlined.
Several practical examples for fault detection and diagnosis of
DC motor drives, a centrifugal pump, automotive suspension and tire
show applications.
General
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