Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network

被引:0
|
作者
Yu, Feifei [1 ]
Chen, Guoyan [1 ]
Dua, Canyi [2 ]
Liu, Liwu [2 ]
Xing, Xiaoting [1 ]
Yang, Xiaoqing [2 ]
机构
[1] Guangdong Polytech Normal Univ, Sch Mechatron Engn, Guangzhou 510665, Peoples R China
[2] GuangDong Polytech Normal Univ, Sch Automot & Transportat Engn, Guangzhou 510665, Peoples R China
基金
中国国家自然科学基金;
关键词
rotating machinery faults; miniature table drilling machine; automobile engine; fault identification; one-dimensional convolutional neural network;
D O I
10.21595/jve.2024.23722
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal.
引用
收藏
页码:793 / 807
页数:15
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