APPLICATION RESEARCH ON LONG SHORT-TERM MEMORY NETWORK IN FAULT DIAGNOSIS

被引：0
|
作者：
Wang, Wei-Feng [1 ]
Qiu, Xue-Huan [1 ]
Chen, Cai-Sen [2 ]
Lin, Bo [1 ]
Zhang, Hui-Min [1 ]
机构：
[1] Army Acad Armored Forces, Dept Informat & Commun, Beijing 100072, Peoples R China
[2] Army Acad Armored Forces, Dept Training Ctr, Beijing 100072, Peoples R China
来源：
PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2 | 2018年
关键词：
LSTM neural network; Fault diagnosis; Deep learning; Machine learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to improve the accuracy of fault diagnosis and accelerate the speed of fault identification, this paper proposes a fault diagnosis method based on long short-term memory (LSTM) neural network, and constructs its architecture and model framework. Taking the gear failure data as the experimental object, the performance of the model is analyzed by adjusting the number of hidden layers, the number of hidden layer neurons, the learning rate, and the training times. In addition, LSTM is compared with support vector machine (SVM), convolutional neural network (CNN) and recurrent neural network (RNN) to verify that the LSTM method has a better classification effect, and the accuracy of fault diagnosis can be increased to 99.80%.
引用
收藏
页码：360 / 365
页数：6
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