Bearing fault diagnosis based on TEO and SVM

A fault method for bearing based on Teager energy operator (TEO) and support vector machine (SVM) is proposed in this paper. First, the total energy of the vibration signal of the bearing is estimated by the TEO technique, which has good time resolution for the instantaneous signal. Then, the Teager spectrums are obtained by applying fast Fourier transform (FFT) to the Teager energy signal. The feature frequencies of different fault modes, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum are extracted to form the feature vectors. Finally, these vectors are introduced into SVM to realize fault classification for the bearing. Experiments are conducted to verify the feasibility of the proposed method, the results show that the proposed method performs effectively to identify the failure mode of the bearing under variable conditions.