A novel two-stream multi-head self-attention convolutional neural network for bearing fault diagnosis

Abstract

Bearing is one of the most prone to failure components, and the most commonly used supporting parts in rotating machinery. There are important economic and safety significance for studying effective bearing fault diagnosis method. Deep learning has been widely applied to intelligent fault diagnosis of rotating machines. In particular, convolutional neural network (CNN) improves diagnostic accuracy due to automatic feature extraction ability. However, CNN cannot fully use the temporal information of the input signals and extract global features. This article presents a new depth model called two-stream multi-head self-attention convolutional neural network (TSMSCNN) for bearing fault diagnosis to solve the above problems. First, we obtain temporal information through the difference of data points in the input signal. Next, the temporal information and original signal are used as input for two CNN branches. Then, the multi-head self-attention (MS) mechanism adaptively aggregates the features and assigns weights to the features extracted by CNN. Finally, the learned high-level representations are fed into the full connect (FC) layer for fault diagnosis. Experimental results prove that the proposed TSMSCNN has higher accuracy than state-of-the-art methods and has excellent noise immunity and transfer performance.