学习迁移
卷积神经网络
计算机科学
传递函数
深度学习
人工智能
方位(导航)
交叉熵
维数(图论)
人工神经网络
算法
断层(地质)
熵(时间箭头)
领域(数学分析)
模式识别(心理学)
深信不疑网络
作者
Jun He,Xiang Li,Yong Chen,Danfeng Chen,Jing Guo,Yan Zhou
摘要
In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.
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