Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults
| Autor: | Zhang Chen, Wang Wei-zhi, Wang Jianguo, Yu Xue, Zhang Chao, Fan Bin, Fengshou Gu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Bearing (mechanical) Computer science business.industry Mechanical Engineering Condition monitoring Pattern recognition 02 engineering and technology Fault (power engineering) Convolutional neural network law.invention Long short term memory 020901 industrial engineering & automation law Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Extraction (military) Artificial intelligence business |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 236:1877-1887 |
| ISSN: | 2041-2983 0954-4062 |
| Popis: | Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN. |
| Databáze: | OpenAIRE |
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