1.School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
2.State Key Lab of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Abstract:Bearing fault diagnosis is very important to ensure the safety of mechanical equipment. In recent years, data-driven fault diagnosis methods have attracted the attention of researchers. Unlike the traditional fault feature extraction methods that rely on expert experience, the deep learning method can realize end-to-end automatic fault feature extraction and classification. In response to the problem that one-dimensional signal cannot fully exploit the relevant information between data when used as input to a convolutional neural network (CNN), a bearing fault diagnosis method based on a MTF-CNN is proposed. The collected vibration signals are encoded by the Markov transition field(MTF), the data correlation in different time intervals is obtained according to the transfer probability between data, and the corresponding feature map is generated. Then, it is input to a CNN to complete feature extraction and fault classification. The model is verified by the bearing dataset of Case Western Reserve University. The experimental results show that the fault diagnosis accuracy of the model is over 99.8%, and better generalization performance is obtained compared to other image coding methods.
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