Multi-scale attention mechanism residual neural network for fault diagnosis of rolling bearings

残余物 人工智能 计算机科学 核(代数) 断层(地质) 模式识别(心理学) 人工神经网络 特征提取 方位(导航) 块(置换群论) 卷积(计算机科学) 深度学习 振动 算法 地质学 物理 地震学 组合数学 量子力学 数学 几何学
作者
Yan Wang,Jie Liang,Xiaoguang Gu,Dan Ling,Haowen Yu
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science [SAGE Publishing]
卷期号:236 (20): 10615-10629 被引量:21
标识
DOI:10.1177/09544062221104598
摘要

Rolling bearing fault diagnosis is crucial to improve industrial safety and reliability. In recent years, intelligent fault diagnosis method represented by deep learning (DL) has been receiving increasing attention. In order to ameliorate the full training of the deep network, improve the model accuracy, and perfect the analysis of mechanical vibration signals with huge amount of information, a multi-scale attention mechanism residual network (MSA-ResNet) fault diagnosis method is proposed in this paper. First, an attention mechanism block is introduced to construct a new type of residual block combination. Second, a multi-scale structure is constructed by choosing an appropriate convolution kernel size. Finally, the overall framework of MSA-ResNet is constructed for efficient training and failure pattern recognition. The MSA-ResNet algorithm introduces an attention mechanism in each residual module of the residual network (ResNet), which improves the sensitivity to features. The features of different scales are obtained through the multi-scale convolution kernel, and the multi-scale feature extraction of complex nonlinear mechanical vibration signals is realized. The processing of original vibration signal rarely involves artificial interference, which is more conducive to industrial application of the proposed method. Diagnostic experiments are conducted on bearing datasets from the Case Western Reserve University (CWRU) and the Machinery Failure Prevention Technology (MFPT) to verify the effectiveness of the proposed method. The results illustrating the rolling bearing fault diagnosis method based on MSA-ResNet have advantages in multi-scale feature extraction, noise immunity, and fault classification accuracy.
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