Fault diagnosis of reciprocating compressor based on group self-attention network

Abstract

Abstract Reciprocating compressors are widely used in the petroleum industry and because of their complex and nonlinear signals, it is difficult to extract fault features. Recently, deep learning has been used in intelligent mechanical fault diagnosis and achieved great success. In the deep learning model, the recursive neural network (RNN) can capture global features, but it is difficult to parallelize and not good at dealing with long sequences. The convolutional neural network (CNN) can capture local features, but its receptive field is limited by the number of layers of the network and the size of the sliding window, resulting in the model not capturing sufficient features. In this paper, we propose a deep learning model without any RNN or CNN structures, called the group self-attention network (GSAN), for fault diagnosis of multisource signals in reciprocating compressors. The GSAN model mainly includes intra-group self-attention, inter-group self-attention and a fusion gate. Among them, intra-group self-attention is used to capture local features within a group, inter-group self-attention is used to capture global features between groups, and the fusion gate finally integrates these features. Experimental results show that compared with other models based on the RNN or the CNNs, the GSAN proposed in this paper not only has higher prediction accuracy, but also better anti-noise performance. In addition, the effectiveness of each part of the model is verified by ablation experiment.