Coordinated by
Vicenç Puig
Silvio Simani
First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
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Library of Congress Control Number: 2021943898
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78945-058-3
ERC Code:
PE7 Systems and Communication Engineering
PE7_1 Control engineering
Introduction
Vicenç PUIG1 and Silvio SIMANI2
1Institute for Robotics and Industrial Informatics, Polytechnic University of Catalonia, Barcelona, Spain
2Department of Engineering, University of Ferrara, Emilia-Romagna, Italy
I.1. Introduction
There is an increasing interest in the theory and applications of model-based fault detection and fault diagnosis methods, because of economical and safety-related matters. In particular, well-established theoretical developments can be seen in many contributions published in the IFAC (International Federation of Automatic Control) Congresses and IFAC Symposium SAFEPROCESS (Fault Detection, Supervision and Safety of Technical Processes) since the seminal editions (Isermann and Ballé 1997; Isermann 1997; Patton 1999; Frank et al. 2000).
The developments began at various places in the early 1970s. Beard (1971) and Jones (1973) reported, for example, the well-known “failure detection filter” approach for linear systems.
A summary of this early development is given by Willsky (1976). Then, Rault et al. (1971) considered the application of identification methods to the fault detection of jet engines. Correlation methods were applied to leak detection (Siebert and Isermann 1976).
The first book on model-based methods for fault detection and diagnosis with specific application to chemical processes was published by Himmelblau (1978). Sensor failure detection based on the inherent analytical redundancy of multiple observers was shown by Clark (1978).
The use of parameter estimation techniques for fault detection of technical systems was demonstrated by Hohmann (1977), Bakiotis et al. (1979), Geiger (1982) and Filbert and Metzger (1982).
The development of process fault detection methods based on modeling, parameter and state estimation was then summarized by Isermann (1984, 1997).
Parity equation-based methods were developed initially by Chow and Willsky (1984) and then further developed by Patton and Chen (1994), Gertler (1991) and Höfling and Pfeufer (1994).
Frequency domain methods are typically applied when the effects of faults as well as disturbances have frequency characteristics that differ from each other and thus the frequency spectra serve as criterion to distinguish the faults (Massoumnia et al. 1989; Ding and Frank 1990; Ding et al. 2000; Frank et al. 2000).
The developments of fault detection and isolation (FDI) methods to the present time are summarized in books by Pau (1981), Patton et al. (2000), Basseville and Nikiforov (1993), Chen and Patton (1999), Gertler (1998) and Isermann (1994b) as well as in survey papers by Gertler (1988), Frank (1990) and Isermann (1994a).
Within IFAC, the increasing interest in this field was taken into account by creating first in 1991 a SAFEPROCESS (Fault Detection Supervision and Safety for Technical Processes) Steering Committee, which then became a Technical Committee in 1993. The first IFAC SAFEPROCESS Symposium was held in Baden–Baden (Germany) in 1991 (Isermann and Freyermuth 1992). The last edition was organized in 2018 in Warsaw (Poland). Another triennial series of IFAC workshops exist for “Fault detection and supervision in the chemical process industries”. Many other thematic workshops have been organized between 1992 and 2020.
Most contributions in fault diagnosis rely on the analytical redundancy principle. The basic idea consists of using an accurate model of the system to mimic the real process behavior. If a fault occurs, the residual signal (i.e. the difference between the real system and model behavior) can be used to diagnose and isolate the malfunction.
Model-based method reliability, which also includes false alarm rejection, is strictly related to the “quality” of the model and measurements exploited for fault diagnosis, as model uncertainty and noisy data can prevent an effective application of analytical redundancy methods.
This is not a simple problem, because model-based fault diagnosis methods are designed to detect any discrepancy between the real system and model behaviors. It is assumed that this discrepancy signal is related to (has a response from) a fault. However, the same difference signal can respond to model mismatch or noise in real measurements, which are erroneously detected as a fault. These considerations have led to research in the field of “robust” methods, in which particular attention is paid to the discrimination between actual faults and errors due to model mismatch.
However, the availability of a “good” model of the monitored system can significantly improve the performance of diagnostic tools, minimizing
