Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions

Wan, Zitong, Yang, Rui ORCID: 0000-0002-5634-5476 and Huang, Mengjie
(2020) Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions. SHOCK AND VIBRATION, 2020. pp. 1-13.

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<jats:p>In the large amount of available data, information insensitive to faults in historical data interferes in gear fault feature extraction. Furthermore, as most of the fault diagnosis models are learned from offline data collected under single/fixed working condition only, this may cause unsatisfactory performance for complex working conditions (including multiple and unknown working conditions) if not properly dealt with. This paper proposes a transfer learning-based fault diagnosis method of gear faults to reduce the negative effects of the abovementioned problems. In the proposed method, a cohesion evaluation method is applied to select sensitive features to the task with a transfer learning-based sparse autoencoder to transfer the knowledge learnt under single working condition to complex working conditions. The experimental results on wind turbine drivetrain diagnostics simulator show that the proposed method is effective in complex working conditions and the achieved results are better than those of traditional algorithms.</jats:p>

Item Type: Article
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 16 Aug 2021 14:20
Last Modified: 18 Jan 2023 21:33
DOI: 10.1155/2020/8884179
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