A novel convolutional neural network with interference suppression for the fault diagnosis of mechanical rotating components

Despite some recent achievements in the intelligent data-driven fault diagnosis of mechanical rotating components, in most cases very large amounts of pure data are demanded for model training. However, the collected vibration signals of mechanical rotating components are inevitably contaminated with noise in real industries. To resolve the low fault diagnosis accuracy due to strong noise interference, a convolutional neural network with interference suppression (ISCNN) is proposed in this paper. First, a parallel convolutional structure with dilated wide convolution kernels is designed to extract long-time correlated fault features of multiple scales from a noise-contaminated signal by sparse sampling, and noise interference is suppressed by filtering operations. Then, a multiscale feature enhancement module is constructed to achieve adaptive tuning of time scale by employing a parallel selective kernel network to exploit weak fault features buried in noisy signals. Finally, a convolutional feature fusion method is adopted to integrate multidimensional fault features and feed them into the classifier, thus achieving accurate fault diagnosis of mechanical rotating components. Experimental results on two benchmark datasets indicate that the ISCNN model outperforms its counterparts in the fault diagnosis field and improves the fault discriminability of mechanical rotating components in strong noisy scenarios.