卷积神经网络
计算机科学
人工智能
深度学习
模式识别(心理学)
块(置换群论)
断层(地质)
卷积(计算机科学)
特征(语言学)
方位(导航)
人工神经网络
一般化
特征提取
过程(计算)
地质学
地震学
哲学
数学分析
几何学
操作系统
语言学
数学
作者
Chao Zhang,Weizhi Wang,Zhang Chen,Bin Fan,Jianguo Wang,Fengshou Gu,Yu Xue
标识
DOI:10.1177/09544062211016505
摘要
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.
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