TY - GEN
T1 - Intelligent fault diagnosis of rolling bearing based on a deep transfer learning network
AU - Wu, Zhenghong
AU - Jiang, Hongkai
AU - Zhang, Sicheng
AU - Wang, Xin
AU - Shao, Haidong
AU - Dou, Haoxuan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Rolling bearing of rotating machinery's key component will inevitably fail due to the complex and changeable operating environment such as variable speed, large disturbance, high and low temperature. It is quite challenging to obtain abundant labeled bearing fault samples because the rotating machinery is typically in a healthy and operational state. For addressing the issue, an intelligent fault diagnosis method based on a deep transfer learning network is proposed. First, a bidirectional gated recurrent unit (Bi-GRU) network is utilized to mine the latent relationship between labeled source domain samples and few labeled target domain samples, the parameters of Bi-GRU are trained to obtain the instance transfer bidirectional gated recurrent unit model (ITBi-GRU), and auxiliary samples are generated based on the ITBi-GRU. Second, as a feature transfer learning method, joint distribution adaptation is used to simultaneously decrease the distribution discrepancies between the generated auxiliary samples and the unlabeled target domain samples. Finally, extensive experiments are employed to evaluate the effectiveness of the proposed method in the case of scarce labeled samples.
AB - Rolling bearing of rotating machinery's key component will inevitably fail due to the complex and changeable operating environment such as variable speed, large disturbance, high and low temperature. It is quite challenging to obtain abundant labeled bearing fault samples because the rotating machinery is typically in a healthy and operational state. For addressing the issue, an intelligent fault diagnosis method based on a deep transfer learning network is proposed. First, a bidirectional gated recurrent unit (Bi-GRU) network is utilized to mine the latent relationship between labeled source domain samples and few labeled target domain samples, the parameters of Bi-GRU are trained to obtain the instance transfer bidirectional gated recurrent unit model (ITBi-GRU), and auxiliary samples are generated based on the ITBi-GRU. Second, as a feature transfer learning method, joint distribution adaptation is used to simultaneously decrease the distribution discrepancies between the generated auxiliary samples and the unlabeled target domain samples. Finally, extensive experiments are employed to evaluate the effectiveness of the proposed method in the case of scarce labeled samples.
KW - Auxiliary samples
KW - Bidirectional gated recurrent unit
KW - Fault diagnosis
KW - Joint distribution adaptation
KW - Rolling bearing
UR - http://www.scopus.com/inward/record.url?scp=85168416296&partnerID=8YFLogxK
U2 - 10.1109/ICPHM57936.2023.10194043
DO - 10.1109/ICPHM57936.2023.10194043
M3 - 会议稿件
AN - SCOPUS:85168416296
T3 - 2023 IEEE International Conference on Prognostics and Health Management, ICPHM 2023
SP - 105
EP - 111
BT - 2023 IEEE International Conference on Prognostics and Health Management, ICPHM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Prognostics and Health Management, ICPHM 2023
Y2 - 5 June 2023 through 7 June 2023
ER -