Rolling bearing fault diagnosis based on multi-label zero-shot learning

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (11) : 55-64.

ZHANG Yonghong1, SHAO Fan1, ZHAO Xiaoping2,3, WANG Lihua1, L Kaiyang2, ZHANG Zhongyang1

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Abstract

Recent years have seen the rapid development of data-driven methods in the field of rolling bearing fault diagnosis. However, there are no target fault samples available for training. This paper proposes the use of multi-label zero-shot learning (MLZSL) fault diagnosis method for solving this problem. MLZSL utilises the short-time Fourier transform (STFT) to pre-process both seen and unseen samples, and inputs the obtained time-frequency images into the residual depthwise separable convolutional neural network (RDSCNN) to perform feature extraction. It then uses the seen fault features for training the attribute learner, and ultimately uses the attribute learner for learning high-dimensional information about unseen faults in order to realise the diagnosis of unseen faults. This paper proposes a fault diagnosis experiment under the conditions of zero samples. The results demonstrate that MLZSL is able to transfer the attributes of seen faults to unseen faults and can then diagnose unseen faults effectively.

Key words

zero-shot learning / feature extraction / multi-label / attribute learner / rolling bearing

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ZHANG Yonghong1, SHAO Fan1, ZHAO Xiaoping2,3, WANG Lihua1, L Kaiyang2, ZHANG Zhongyang1. Rolling bearing fault diagnosis based on multi-label zero-shot learning[J]. Journal of Vibration and Shock, 2022, 41(11): 55-64

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