Enhancing bearing fault diagnosis using motor current signals: A novel approach combining time shifting and CausalConvNets

Abstract

In motor drive system, Bearing fault detection through motor current signal (MCS) analysis has gained recognition for its cost-effectiveness and non-invasive nature. However, two-dimensional (2D) convolutional neural networks (CNNs) exhibit suboptimal performance when directly applied to raw stator current signals for fault detection. Meanwhile, the training of Recurrent Neural Networks (RNNs) for processing one-dimensional (1D) temporal sequence can be a time-consuming task due to the inherent procedures of the network structure. To balance time-consuming and accuracy, this paper presents a novel MCS-based bearing fault detection model utilizing a 1D CNN and corresponding pre-processing methods. In the early stages, a noise cancellation strategy termed time shifting is implemented. Subsequently, wavelet transform is employed to further refine the processed signal by eliminating components with minimal significance. By compressing homogenous signals, the resultant tensor, referred to as the “root map”, is employed as the input for the proposed temporal causal network. Two groups of experimental results conducted on both laboratory and industrial conditions validate the effectiveness and convenience of the proposed methodology.