《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Cross-domain fault diagnosis of rotating machinery based on deep features selection

Published:2022-12-20 author:HE Cai-lin, FEI Guo-hua, ZHU Jian, et al. Browse: 453 Check PDF documents

Cross-domain fault diagnosis of rotating machinery

based on deep features selection

HE Cai-lin1, FEI Guo-hua2, ZHU Jian3, DONG Fei4, SONG Jun-cai4

(1.School of Mechanical and Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312099,

China; 2.Jiaxing Technician Institute, Jiaxing 314001, China; 3.Hangzhou First Technician College,

Hangzhou 310023, China; 4.School of Internet, Anhui University, Hefei 230039, China)

Abstract: In practical industrial scenarios, when diagnosing rotating machinery faults, there were problems such as insufficient labeled fault samples and differences in data distribution. For this reason, a new crossdomain fault diagnosis was proposed based on deep feature selection and transfer learning methods. Firstly, the deep feature extraction was carried out by using the deep autoencoder, and the deep feature pool was constructed by using the deep features extracted by the deep autoencoder under different activation functions. Then, the proposed features selection method for cross domain diagnosis was used to select transferable features for the subsequent feature transfer learning. The proposed improved joint distribution adaptation was used to reduce the distribution differences between source and target domains. Finally, based on the labeled source domain samples and unlabeled target domain samples after transfer learning, the fault recognition classifier was trained, and the cross-domain fault diagnosis experiment was carried out through the bearing and motor fault data of the mechanical fault simulation test-bed. The research results show that the proposed diagnosis method can achieve the better cross-domain fault diagnosis performance than the comparison models, and its maximum fault diagnosis accuracy (bearing: 95.42%, motor: 88.67%) is significantly higher than other comparative models when the suitable number of features were selected.

Key words: rotating machinery; insufficient labeled fault samples; deep features selection(DFS); improved joint distribution adaptation(IJDA); multiple kernel-maximum mean discrepancy(MK-MMD); transfer learning(TL) method; deep auto-encoder (DAE)

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