A fault diagnosis method with AT-ICNN based on a hybrid attention mechanism and improved convolutional layers☆

被引:8
|
作者
Li, Xueyi [1 ,3 ]
Xiao, Shuquan [1 ]
Zhang, Feibin [2 ]
Huang, Jinfeng
Xie, Zhijie [1 ]
Kong, Xiangwei [3 ,4 ]
机构
[1] Northeast Forestry Univ, Coll Mech & Elect Engn, Harbin 150040, Peoples R China
[2] Tsinghua Univ, Dept Mech Engn, Beijing 100084, Peoples R China
[3] Northeastern Univ, Key Lab Vibrat & Control Aeroprop Syst, Minist Educ, Shenyang 110819, Peoples R China
[4] Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
Deep learning; Fault diagnosis; Hybrid attention mechanism; Improved convolutional layers;
D O I
10.1016/j.apacoust.2024.110191
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Fault diagnosis is crucial for mechanical systems, with early diagnosis of bearings playing a key role in ensuring the overall safety and smooth operation of the mechanical system. However, in real industrial environments, traditional diagnostic methods limit the extraction of fault signals from rotating machinery. This study aims to improve the fault diagnosis method for critical mechanical components and proposes a novel deep learning model, the Attention Improved CNN (AT-ICNN) fault diagnosis method. The method combines Convolutional Neural Network (CNN) and attention mechanism to extract key fault feature information from signals, enhancing the model's ability to highlight fault features and capture global information. This improves the accuracy of fault type identification. The AT-ICNN model enhances traditional CNN models by introducing Improved Convolutional (IMConv) and integrating a hybrid attention mechanism to effectively extract relevant fault information. Experimental results demonstrate superior diagnostic performance of AT-ICNN on the CWRU bearing dataset and laboratory bearing dataset, with accuracy rates of 98.12% and 98.72%, respectively. This represents about 9% improvement over baseline models and other advanced methods. In-depth analysis of experimental results validates the significant advantages of AT-ICNN in the field of fault diagnosis for critical mechanical components.
引用
收藏
页数:12
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