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Multi-label fault diagnosis of rolling bearing based on meta-learning

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Abstract

In practical applications, it is difficult to acquire sufficient fault samples for training deep learning fault diagnosis model of rolling bearing. Aiming at the few-shot issue and multi-label attributes of single-point faults, a novel fault diagnosis method of rolling bearing based on time–frequency signature matrix (T–FSM) feature and multi-label convolutional neural network with meta-learning (MLCML) is proposed in this paper. At the beginning, the T–FSM features sensitive to few-shot fault diagnosis of measured vibration signal are extracted. Subsequently, a designed multi-label convolutional neural network (MLCNN) with a specific architecture is employed to identify faults. Crucially, the meta-learning strategy of learning initial network parameters susceptive to task changes is incorporated to MLCNN for addressing the few-shot problem. Ultimately, the publicly available rolling bearing dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results exhibit that the trained MLCML has the capability of learning to learn few-shot fault attributes with outstanding diagnosis accuracy and generalization. More concretely, the model can adapt to new fault categories rapidly owing to that only a few samples and update steps are required to fine-tune the network.

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Acknowledgements

This study was funded by Natural Science Foundation of Beijing Municipality (4202015) and the Postgraduate Research Capacity Improvement Program from Beijing Technology and Business University in 2020.

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, People’s Republic of China

    Chongchong Yu, Yaqian Ning, Weijun Su & Xia Zhao

  2. Key Laboratory of Industrial Internet and Big Data (Beijing Technology and Business University), China National Light Industry, Beijing, 100048, People’s Republic of China

    Chongchong Yu

  3. State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Yong Qin

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  1. Chongchong Yu
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Correspondence to Chongchong Yu.

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Yu, C., Ning, Y., Qin, Y. et al. Multi-label fault diagnosis of rolling bearing based on meta-learning. Neural Comput & Applic 33, 5393–5407 (2021). https://doi.org/10.1007/s00521-020-05345-0

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