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

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...

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Bibliographic Details
Main Authors: Zitong Wan, Rui Yang, Mengjie Huang
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2020/8884179
Description
Summary: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.
ISSN:1070-9622
1875-9203