A lightweight model for train bearing fault diagnosis based on multiscale attentional feature fusion. (1st February 2023)

• Record Type: Journal Article
• Authors: He, Changfu; He, Deqiang; Lao, Zhenpeng; Wei, Zexian; Xiang, Zaiyu; Xiang, Weibin
• Is Part Of: Measurement science & technology. Volume 34:Number 2(2023)
• Publication Date: 2023-02-01
• Subjects: high-speed train -- rolling bearing -- fault diagnosis -- neural network -- feature fusion -- lightweight; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aca170
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: As one of the key components of a train, the running gear bearing has the highest fault rate, and its health condition is very important for the safe operation of the train. Therefore, how to quickly and accurately diagnose the health condition of the train running gear bearings under strong noise and variable working conditions has become one of the core contents of the intelligent operation and maintenance strategy. To meet these requirements, a lightweight convolutional neural network based on multiscale attentional feature fusion (MA-LCNN) is proposed in this paper, which takes the inverted residual network as the main structure. Firstly, a multiscale attention module (MA) was designed to extract fault feature information. Secondly, by embedding MAs in different locations, the ability of the MA-LCNN to extract fault feature information is greatly improved. Finally, an ablation experiment and noise resistance experiment are performed. The recognition accuracy scores of the MA-LCNN for cases 2 and 3 are 99.70% and 99.83%, respectively. The results show that the proposed attention module has better learning ability and stability compared to the contrast modules. The MA-LCNN demonstrates better fault diagnosis performance than contrast models under different noise environments and variable working conditions.