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Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network

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Abstract

The success of convolutional neural networks (CNNs) in intelligent fault diagnosis is largely dependent on massive amounts of labelled data. In a real-world case, however, massive amounts of labelled data are difficult or costly to collect, whereas abundant unlabelled data are often available. To utilize such unlabelled data, a novel method using a semi-supervised convolutional neural network (SSCNN) for intelligent fault diagnosis of bearings is proposed. First, a 1-d CNN is applied to learn class space features and generate class probabilities of unlabelled samples, based on which a class probability maximum margin criterion (CPMMC) method is used to construct the loss function of unlabelled samples. Then, the constructed loss function, which aims to maximise the inter-class distance of class space features and minimise the intra-class distance of class space features, is integrated into the cross-entropy loss function of the CNN, and the SSCNN is established. Finally, the SSCNN model is applied to analyse the vibration signals collected from rolling bearings, and a novel intelligent fault diagnosis method using the SSCNN is proposed. Two datasets are employed to validate the effectiveness of the proposed methodology. The results show that the established SSCNN can effectively utilise unlabelled samples to train the model and enhance its fault diagnosis performance. Through a comparison with commonly used semi-supervised deep learning methods, the superiority of the proposed method is validated.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 51675253) and China Postdoctoral Science Foundation (No. 2016 M592857).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Yaochun Wu, Rongzhen Zhao, Wuyin Jin, Tianjing He, Sencai Ma & Mingkuan Shi

  2. School of Mechanical Engineering, Anyang Institute of Technology, Anyang, 455000, China

    Yaochun Wu

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  1. Yaochun Wu
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  2. Rongzhen Zhao
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  3. Wuyin Jin
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  4. Tianjing He
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Correspondence to Rongzhen Zhao.

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Wu, Y., Zhao, R., Jin, W. et al. Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network. Appl Intell 51, 2144–2160 (2021). https://doi.org/10.1007/s10489-020-02006-6

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