Observability and Redundancy Decomposition Application to Diagnosis
The safety of processes can be greatly enhanced through the detection and isolation of the changes indicative of modifications in the process performances. If the models describing the process are accurate, the problem of fault detection may be solved by observer-type filters. These filters generate the so-called residuals computed from the inputs and the outputs of the process. The generation of these residual signals is the first stage in the problem of FDI, as described in Chapter 2. To be useful in the FDI task, the residuals must be insensitive to modelling errors and highly sensitive to the faults under consideration. In that regard, the residuals are designed so that the effects of possible faults are enhanced which in turn increases their detectability. The residuals must also respond quickly. The second stage of FDI is concerned with residual analysis and decision making; the residuals are tested in order to detect the presence of faults. The use of simple decision rules such as threshold tests or more sophisticated approaches using pattern recognition, sequential probability ratio test or sensitivity analysis is very helpful at this stage. Various FDI method have been reported in the literature, notably in the excellent survey papers of Willsky (1976), Isermann (1984), Frank (1990a), Gertler (1988, 1991), Patton (1991, 1997). Among the classical books on the subject are those of Himmelblau (1978), Pau (1981), Basseville (1986), Patton et al. (1989), Dubuisson (1990). The particular point of view of data reconciliation is addressed in the book of Ragot et al. (1990).
KeywordsFault Detection Incidence Matrix Sensor Fault Parity Space Actuator Fault
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