Application of the Motor Abnormal Sound Detection Based on Wavelet Packet and SVM One-Class Learning

A large number of small motors are detected in production line by trained workers for abnormal sound.According to the statistical characteristics of the motor sound signal and artificial detection,the acoustic signal of motor is decomposed by the method of wavelet packet transform,and its singular value is extracted as the characteristics of frequency band coefficients to be mapped to the state space generated by the feature vector.Because it is difficult to analyze the less abnormal noise motor samples,difficult acquirement and abnormal noise caused by the indicidual differences in production line,the support vector machine(SVM) one-class learning algorithm is used to detect the abnormal sound.Through analyzing the measured motor sound signals and making full use of the excellent time-frequency localization characteristics of wavelet packet and the excellent study performance and global optimal ability of SVM under the condition of small sample,the effectiveness of this approach is verified.