Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD

Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are low efficiency and poor accuracy. To address these problems, a new adaptive and intelligent mechanical fault diagnosis method for DC motors based on variational mode decomposition (VMD), singular value decomposition (SVD), and residual deep convolutional neural networks with wide first-layer kernels (R-WDCNN) was proposed. First, the vibration signals of a DC motor were collected by a designed acquisition system. Subsequently, VMD was employed to decompose the raw signals adaptively into several intrinsic mode functions (IMFs). Moreover, the transient frequency means method, which can quickly and accurately obtain the optimal value of K, is proposed. SVD was applied to reduce the dimensionality of the IMF matrix for further feature extraction. Finally, the reconstructed matrix containing the main fault feature information was used to train and test the R-WDCNN. Based on residual learning, identification and classification of four types of vibration signals were achieved, while the R-WDCNN was optimized by the adaptive batch normalization algorithm (AdaBN). The recognition rate and the convergence were improved by this classifier. The results show that the method proposed in this paper has better adaptability and intelligence than other methods, and the R-WDCNN can reach a 94% recognition rate on unknown samples. Therefore, the proposed method is more intelligent and accurate than other methods.