A semi-supervised fault diagnosis model based on a teacher-student network
GAO Yucai1,FU Zhongguang1,XIE Yucun2,WANG Shiyun1
1.Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education, North China Electric Power University, Beijing 102206, China;
2.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Beijing 102206, China
Abstract：This paper proposes a semi-supervised learning method based on continuous wavelet transform and teacher-student network for fault diagnosis of rotating machinery, aiming to address the problems of network overfitting, low fault diagnosis accuracy, and underutilization of large amounts of unlabeled data when neural network models are used with limited labeled samples. The method is based on an improved Lenet-5 convolutional neural network model, which establishes a student network model and a teacher network model with the same structure and initialization parameters. First, the vibration signal of rotating machinery is transformed by continuous wavelet transform into a three-dimensional time-frequency image. Then, pseudo-labels are generated using the prediction results of the teacher model, and these pseudo-labels are combined with the real labels to train the student network. At the same time, the teacher network model parameters are updated using exponential weighted moving average algorithm. The experimental results show that compared with the pure supervised learning model, the proposed algorithm can significantly improve the stability of the model training process and the accuracy of fault diagnosis with limited labeled samples.
收稿日期: 2023-03-29
出版日期: 2024-02-28
引用本文:
高玉才1，付忠广1，谢玉存2，王诗云1. 基于教师-学生网络的半监督故障诊断模型[J]. 振动与冲击, 2024, 43(4): 150-157.
GAO Yucai1,FU Zhongguang1,XIE Yucun2,WANG Shiyun1. A semi-supervised fault diagnosis model based on a teacher-student network. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(4): 150-157.
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