Abstract:A rolling bearing fault diagnosis method based on Gramian angular field image coding and transfer deep residual neural network was proposed to solve the problems of over-fitting phenomenon of traditional convolutional neural network and time information loss of traditional grayscale image coding in fault diagnosis. According to the uniqueness of Gramian angular field image coding method for time series coding mapping, the original vibration signal is transformed into Gramian angular difference fields diagram and Gramian angular summation fields diagram, and the ResNet18 model parameters pre-trained on ImageNet are transferred to ResNet18 which takes the Gramian angular field diagram as input, and performs feature extraction and classification of the Gramian angular field diagram under different failure modes, so as to achieve the purpose of fault diagnosis. The analysis results show that the proposed method can highlight the intrinsic features of different fault patterns better than the traditional grayscale graph coding, and has a higher recognition accuracy of 99.30% with faster convergence and stronger robustness compared with the traditional convolutional neural network model.
Key words: Gramian angular fields; image coding; transfer deep residual neural network; rolling bearing; fault diagnosis
古莹奎,吴宽,李成. 基于格拉姆角场和迁移深度残差神经网络的滚动轴承故障诊断[J]. 振动与冲击, 2022, 41(21): 228-237.
GU Yingkui, WU Kuan, LI Cheng. Rolling bearing fault diagnosis based on Gram angle field and transfer deep residual neural network. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(21): 228-237.
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