Early Fault Identification of Aircraft and Self-Recovery Monitoring of Fault

Abstract

In order to increase robustness of the aircraft and to solve the problem of lacking fault samples in fault diagnosis and the difficulty in identifying early weak fault, we proposed a new method for identifying the early fault of the aircraft and it can do self-recovery monitoring of the fault. Our method is based on the analysis of the characteristics of early fault of the aircraft, and it combined the SVM (support vector machine) with the stochastic resonance theory and the wavelet packet decomposition. First, we zoom the early fault feature signals by using the stochastic resonance theory. Second, we extract feature vectors of the early fault by using the multi-resolution analysis of the wavelet packet. Third, we input the feature vectors to a fault classifier, which can be used to identify the early fault of the aircraft quickly and do self-recovery monitor of fault. In this paper, feature of early fault on aircraft, the zoom of early fault characteristics, the extraction method of early fault feature, the construction of multi-fault classifier and way of fault self-recovery monitoring are studied. Results show that our method can effectively identify the early fault of aircraft, especially for identifying of fault with small samples, and it can carry on monitoring of fault self-recovery.