Long-range Dependencies Learning Based on Non-Local 1D-Convolutional Neural Network for Rolling Bearing Fault Diagnosis
Huan Wang, Zhiliang Liu, Ting Ai
Abstract
In the field of data-driven bearing fault diagnosis, convolutional neural network (CNN) has been widely researched and applied due to its superior feature extraction and classification ability. However, the convolutional operation could only process a local neighborhood at a time and thus lack ability of capturing long-range dependencies. Therefore, building an efficient learning method for long-range dependencies is crucial to comprehend and express signal features considering that the vibration signals obtained in a real industrial environment always have strong instability, periodicity, and temporal correlation. This paper introduces non-local mean to the CNN and presents 1D non-local block (1D-NLB) to extract long-range dependencies. The 1D-NLB computes the response at a position as a weighted average value of the features at all positions. Based on it, we propose a non-local 1D convolutional neural network (NL-1DCNN) aiming at rolling bearing fault diagnosis. Furthermore, the 1D-NLB could be simply plugged into most existing deep learning architecture to improve their fault diagnosis ability. Under multiple noise conditions, the 1D-NLB improves the performance of the CNN on the wheelset bearing dataset of high-speed train and the Case Western Reserve University bearing dataset. The experiment results show that the NL-1DCNN exhibits superior results compared with six state-of-the-art fault diagnosis methods.