A novel framework for bearing fault diagnosis across working conditions based on time-frequency fusion and multi-sensor data fusion

被引:5
作者
Lin, Bo [1 ,2 ]
Zhu, Guanhua [3 ]
Zhang, Qinghua [3 ]
Sun, Guoxi [3 ]
机构
[1] Guangdong Univ Petrochem Technol, Sch Automat, Maoming 525000, Guangdong, Peoples R China
[2] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
[3] Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment & Fault, Maoming 525000, Guangdong, Peoples R China
关键词
bearing fault diagnosis; time-frequency fusion; multi-sensor data fusion; deep learning;
D O I
10.1088/1361-6501/ad75ae
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The condition of bearings significantly impacts the healthy operation of rotating machinery. However, bearings are prone to failure under a harsh working environment and alternating load. Integrating time-domain, frequency-domain, and multi-sensor data information has been a remarkable way to improve the accuracy and robustness of bearing fault diagnosis. How to combine these pieces of information remains a significant challenge. A novel network architecture called time-frequency multi-sensor fusion network is developed to address this issue. Firstly, a multi-scale feature extraction module based on a one-dimensional convolutional neural network is proposed for extracting multi-scale information from time-domain signals. Secondly, a multi-sensor data fusion strategy based on scaled dot product attention is applied to facilitate feature interaction among multi-sensor data. Thirdly, a time-frequency fusion module is designed to fuse the time-domain and frequency-domain features from multi-sensor. Finally, the effectiveness and superiority of the proposed method are validated on the Paderborn dataset.
引用
收藏
页数:14
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