Improved multi-task fault diagnosis of hydraulic pump and rolling bearing with multiple sample sizes

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (4) : 270-278.

ZHENG Zhi1, ZENG Kuikui1, HE Yuling2, LI Ke1, WANG Zhijun1

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Abstract

The multiple equipment components based on sufficient samples result in a large scale of multi-task learning network, and the slight and serious cross-component zero-sample problem has not been studied and is difficult. Under multiple sample sizes (sufficient samples and zero-sample), aiming at the problem that the scale of multi-component diagnosis network based on sufficient fault samples is too large, the MicroNet method is introduced to lighten the MTL network, and then the network is optimized by cosine annealing with warm restart algorithm, the lightweight MTL network model is proposed, thus the accuracy and efficiency of MTL network are improved. Aiming at the more difficult cross-component zero-sample problem, the meta-learning method is introduced to further improve the above MT-MN-CA, and then the improved lightweight MTL network model is proposed to solve the slight and serious cross-component zero-sample problem. The effectiveness and superiority of the proposed two network models are verified by the measured faults of hydraulic pump and rolling bearing, the experimental results show that the proposed network has high real-time performance and accuracy.

Key words

multi-task learning / lightweight / meta-learning / zero-sample / fault diagnosis

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ZHENG Zhi1, ZENG Kuikui1, HE Yuling2, LI Ke1, WANG Zhijun1. Improved multi-task fault diagnosis of hydraulic pump and rolling bearing with multiple sample sizes[J]. Journal of Vibration and Shock, 2024, 43(4): 270-278

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