Gear crack fault diagnosis of convergent planetary rowbased on 1-D depth residual shrinkage network

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (19) : 198-206.

TIAN Qinwen1, FENG Fuzhou2, LI Ming2, CHEN Xiaoming2, ZHU Junzhen2, HU Hao2, SONG Chao2

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Abstract

When cracks appear in the catchment star row of armored vehicles, there is a lot of interference on the surface of the box, and there is a certain deviation in the common fault diagnosis methods. For this reason, a crack diagnosis model of confluence planetary gear based on one-dimensional depth residual shrinkage network is proposed. Its characteristic is that it combines the attention mechanism with soft threshold to enhance the useful information, restrain the redundant information, and introduce it into the residual neural network to significantly improve the ability of model feature extraction. In order to verify the feasibility of the model, the vibration signals of four kinds of planetary gear cracks are collected as samples for fault diagnosis. The results show that this method can achieve higher accuracy in a shorter time for the vibration signal of the confluence planetary gear box, and can achieve better classification results than other methods.
Key words: Deep Learning; Convergent Planetary Bar; Crack Fault Diagnosis

Key words

Deep Learning / Convergent Planetary Bar / Crack Fault Diagnosis

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TIAN Qinwen1, FENG Fuzhou2, LI Ming2, CHEN Xiaoming2, ZHU Junzhen2, HU Hao2, SONG Chao2. Gear crack fault diagnosis of convergent planetary rowbased on 1-D depth residual shrinkage network[J]. Journal of Vibration and Shock, 2022, 41(19): 198-206

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