A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis. (June 2021)

• Record Type: Journal Article
• Authors: Qian, Quan; Qin, Yi; Wang, Yi; Liu, Fuqiang
• Is Part Of: Measurement. Volume 178(2021)
• Publication Date: 2021-06
• Subjects: Intelligent fault diagnosis -- Convolutional auto-encoder -- Deep transfer learning -- Anti-noise ability -- Domain adaption; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109352
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: CAE is first designed as the feature extractor to mine the fault features. Both CORAL loss and domain classifier loss are integrated for domain adaptation. A new fault transfer diagnosis model called CAE-DTLN is constructed. The proposed model can be well applied to mechanical fault transfer diagnosis. Abstract: Deep learning has gained a great achievement in the intelligent fault diagnosis of rotating machineries. However, the labeled data is scarce in actual engineering and the marginal distribution of data is discrepant under different conditions. Transfer learning provides a feasible way to overcome these difficulties. Considering the effect of noise on the transfer fault diagnosis, this work puts forward a new deep transfer learning network based on convolutional auto-encoder(CAE-DTLN) to implement the mechanical fault diagnosis in target domain without labeled data. In the proposed framework, CAE is used as the feature extractor as it has the ability of noise removal. Moreover, both CORrelation ALignment (CORAL) loss and domain classification loss are integrated to enhance the effect of domain confusion. The proposed model is applied to the fault transfer diagnosis of planetary gearboxes under different working loads and noise levels, and it is compared with other typical fault transfer diagnosis models. The experimental results show that CAE-DTLN has higher diagnosis accuracy and stronger generalization ability. The average diagnostic accuracy of CAE-DTLN is over 99%. Moreover, the proposed transfer learning model has better anti-noise performance.