Selective-Reinitialization Multiple-Model Adaptive Estimation for Fault Detection and Diagnosis

The existing multiple-model adaptive estimation approach is able to detect faults quickly. However, there are three main problems when it is used for fault detection and diagnosis: false alarms, requirement of designing additional models to identify the faults, and slow response to detect the removal of the faults. In this paper, a novel selective-reinitialization multiple-model adaptive estimation approach is proposed. This approach introduces a state augmentation strategy that can identify the faults without designing additional models, as well as reduce false alarms. The major contribution of this approach is that three selective-reinitialization algorithms are proposed that can improve the performance of the multiple-model adaptive estimation significantly. The selective-reinitialization multiple-model adaptive estimation approach eliminates false alarms and is quick to detect the removal of the faults. The performance of the proposed approach is compared with the multiple-model adaptive estimation an...

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