A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.
Railway rolling bearing fault diagnosis based on multi-scale intrinsic mode function permutation entropy and extreme learning machine classifier
