Planetary gearbox fault diagnosis method based on deep belief network transfer learning

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (1) : 127-133.

CHEN Renxiang1,2, YANG Xing1, HU Xiaolin3, LI Jun1, CHEN Cai4, TANG Linlin1

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Abstract

Planetary gearbox is affected by many factors, such as, working condition and operational situation in practice. The obtained data are difficult to meet the condition of training and test data being independent and identically distributed and training data being sufficient. All these directly affect planetary gearbox fault diagnosis effect. Here, a planetary gearbox fault diagnosis method based on deep belief network (DBN) transfer learning was proposed. Firstly, the original signal spectrum of the auxiliary marker data was taken as the input of the DBN network. The weight and bias value of the network were updated layer by layer to hierarchically express the input signal and obtain its distributed feature expression, and a DBN pre-model based on the auxiliary marker samples was obtained. Then, a small number of target labeled samples were used to finely tune the network weight and bias value of the DBN pre-model, and realize the migration of the weight and bias value of the DBN network from the source domain to the target domain for the purpose of adapting to the new target sample recognition and finally improving the correctness rate of the target domain sample fault identification. Finally, the feasibility and effectiveness of the proposed method were verified with fault simulation tests of planetary gearbox.

Key words

planetary gearbox / fault diagnosis / deep belief network (DBN) / transfer learning

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CHEN Renxiang1,2, YANG Xing1, HU Xiaolin3, LI Jun1, CHEN Cai4, TANG Linlin1. Planetary gearbox fault diagnosis method based on deep belief network transfer learning[J]. Journal of Vibration and Shock, 2021, 40(1): 127-133

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