Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief Causal fault detection and isolation based on a set-membership approach
Automatica (Journal of IFAC)
International Journal of Applied Mathematics and Computer Science
Computer Methods and Programs in Biomedicine
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One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the system's behavior, obtained from the measurements, and the model's behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is SemiQUALitative TRACKing (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project Advanced Decision Support System for Chemical/Petrochemical Manufacturing Processes and are also described in this paper.