Multi-position and multi-type fault diagnosis method of rolling bearing based on spatio-temporal features

被引：0
|
作者：
Peng, Cheng [1 ]
Li, Lingling [1 ]
Chen, Yufeng [1 ]
Man, Junfeng [2 ]
机构：
[1] School of Computer, Hunan University of Technology, Zhuzhou
[2] School of Computer Science, Hunan First Normal University, Changsha
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 09期
基金：
中国国家自然科学基金;
关键词：
long short term memory network; multi-fault classification; one-dimensional full convolution network; rolling bearing; spatio-temporal features;
D O I：
10.13196/j.cims.2022.0073
中图分类号：
学科分类号：
摘要：
Aiming at the challenge of multi-position and multi-type fault diagnosis of rolling bearing, a method of rolling hearing fault diagnosis based on spatio-temporal feature fusion was proposed. The Long Short Term Memory network (LSTM) was adopted to extract the time series features of the bearing data set, and the improved one-Dimensional Full Convolution Network (1D-FCN) was used to extract the spatial features of the vibration acceleration signal of the rolling bearing. Then, the innovative full connection layer algorithm was designed to fuse the spatial and temporal features and update the network parameters. Finally, the proposed multi-classification algorithm was applied to identify different positions and different fault types of the rolling bearing. Experimental results showed that the proposed method had more significant feature extraction ability than Convolutional Neural Network (CNN), Support Vector Machine (SVM) and other methods, and the final classification accuracy was better than the above traditional methods, which proved the effectiveness and superiority of this method. © 2024 CIMS. All rights reserved.
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收藏
页码：3221 / 3231
页数：10
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