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Gearbox fault detection based on pre-trained GoogleNet modal and transfer learning

Published:2024-03-06 author:YANG Weihua, RUAN Aiguo, HUANG Guoyong. Browse: 1205 Check PDF documents

Gearbox fault detection based on pre-trained GoogleNet modal

and transfer learning

YANG Weihua1,2, RUAN Aiguo3, HUANG Guoyong1

(1.Faculty of Civil Aviation and Aviation, Kunming University of Science and Technology, Kunming 650500, China;

2.Wenshan Miao Zhuang Autonomous Prefecture Advanced Technical School, Wenshan 663000, China; 3.China General

Nuclear Power New Energy Holding Co., Ltd., Yunnan Branch, Kunming, 650000, China)

Abstract: In order to address the issues of cumbersome feature processing algorithms and the significant human factors in the diagnosis of gearbox, a fault diagnosis method based on a pre-training model and transfer learning (TL) was proposed. Firstly, the continuous wavelet transform (CWT) was used to convert discrete time series into two-dimensional wavelet scale images to construct a sample set. Then, the pre-trained model was structurally fine-tuned and parameter-tuned to meet the task requirements. The fine-tuned model was further trained with processed training samples until the desired accuracy was achieved and the corresponding model was saved and applied to fault classification tasks. Finally, the fine-tuned model was validated by using parallel gearbox data from key laboratory of control and optimization of Kunming University of Science and Technology and the planetary gearbox data of Southeast University. The research results demonstrate that comparing to traditional convolutional neural networks (CNN) and nonpretrained GoogleNet models, the proposed model achieves an average classification accuracy of 97.40% with limited training samples. Additionally, the proposed model exhibits the characteristics of faster convergence speed, lower dependence on computational power since upper layers of model is modified. The method of fine-tuning the upper level of the proposed model can personalize output according to the task classification, so that the proposed model can be applied in different scenarios.
Key words: speed transmission; pretrained networks; transfer learning(TL); continuous wavelet transform(CWT); scalogram; convolutional neural network(CNN)

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