Abstract
In the last two decades, model-based fault detection and isolation (FDI) has been investigated intensively (Frank, 1990; Chen and Patton, 1999; Patton et al., 1989). These methods require both a nominal model of the system considered, i.e., a model of the system under its normal operating conditions, and models of the system under its faulty conditions. The nominal model is used in the fault detection step to generate residuals, defined as a difference between the output signals of the system and its model. The analysis of these residuals gives an answer to the question whether a fault occurs or not. If it does occur, the fault isolation step is performed in a similar way analyzing residual sequences generated with the models of the system under its faulty conditions (Fig. 10.1).
This work was partially supported by the European Union in the framework of the FP 5 RTN: Development and Application of Methods for Actuators Diagnosis in Industrial Control Systems, DAMADICS (2000–2003), and within the grant of the State Committee for Scientific Research in Poland, KBN, No. 131/E-372/SPUB-M/5 PR UE/DZ 58/200l.
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Janczak, A. (2004). Parametric and Neural Network Wiener and Hammerstein Models in Fault Detection and Isolation. In: Korbicz, J., Kowalczuk, Z., Kościelny, J.M., Cholewa, W. (eds) Fault Diagnosis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18615-8_10
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