Chapter 4. FDI Approach

Abstract

Model-based Fault Detection and Isolation (FDI) of dynamic systems is based on the use of models (analytical redundancy) to check the consistency of observed behaviors. This consistency check is based on computing the difference between the predicted value from the model and the real value measured by the sensors. Then, this difference, known as residual, is compared with a threshold value (zero in the ideal case). When the residual is greater than the threshold, it is considered that there is a fault in the system. Otherwise, it is considered that either the system is working properly or, if it is faulty, the fault cannot be detected. This is denoted as residual evaluation. Due to the presence of noise, disturbances, and model errors, the residuals are never zero, even if there is no fault. Therefore, the detection decision requires testing the residual against thresholds, obtained empirically or by theoretical considerations. Also the desensitizing of the residual from the noise, the disturbances, and the model errors while maximizing fault sensitivity is the goal of the robust design of the detection and diagnosis algorithms. Fault detection is followed by the fault isolation procedure which intends to distinguish a particular fault from others. While a single residual is sufficient to detect faults, a set (or a vector) of residuals is required for fault isolation [13]. If a fault can be distinguished from other faults using a residual set, then it is said that this fault is isolable