A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults

被引:4
|
作者
Xin, Ruihao [1 ]
Feng, Xin [2 ]
Wang, Tiantian [1 ]
Miao, Fengbo [1 ]
Yu, Cuinan [3 ]
机构
[1] Jilin Inst Chem Technol, Sch Informat & Control Engn, Jilin 132022, Peoples R China
[2] Jilin Inst Chem Technol, Sch Sci, Jilin 132022, Peoples R China
[3] Jilin Univ, Sch Software, Changchun 130015, Peoples R China
基金
中国国家自然科学基金;
关键词
fault diagnosis; multi-task; information fusion; multi-layer attention; MODE DECOMPOSITION; TRANSFORM; ATTENTION;
D O I
10.3390/machines11020198
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The use of deep learning for fault diagnosis is already a common approach. However, integrating discriminative information of fault types and scales into deep learning models for rich multitask fault feature diagnosis still deserves attention. In this study, a deep multitask-based multiscale feature fusion network model (MEAT) is proposed to address the limitations and poor adaptability of traditional convolutional neural network models for complex jobs. The model performed multidimensional feature extraction through convolution at different scales to obtain different levels of fault information, used a hierarchical attention mechanism to weight the fusion of features to achieve an accuracy of 99.95% for the total task of fault six classification, and considered two subtasks in fault classification to discriminate fault size and fault type through multi-task mapping decomposition. Of these, the highest accuracy of fault size classification reached 100%. In addition, Precision, ReCall, and Sacore F1 all reached the index of 1, which achieved the accurate diagnosis of bearing faults.
引用
收藏
页数:20
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