Fault diagnosis based on hierarchical clustering support vector machine for helicopter rotor

Due to weak fault signal and difficulty in extracting fault feature for helicopter rotor, the wavelet package analysis is adopted to eliminate the noise in the actual signals and to extract energy feature vector in various frequency bands. And considering the region of rejection existing in current multi-class support vector machine (SVM) classification algorithm, a new support vector machine based on hierarchical clustering and decision tree is proposed to solve the multi-class recognition problems in helicopter rotor fault diagnosis. The experiment results indicated that wavelet package can eliminate the noise in helicopter vibration signal and that the presented multi-class SVM simplified categorizer structure, avoided the region of rejection, and accelerated the training and identifying speed.