TY - GEN
T1 - Adversarial Based Unsupervised Domain Adaptation for Bearing Fault Diagnosis
AU - Wang, Hongshu
AU - Yang, Rui
N1 - Funding Information:
This work is partially supported by National Natural Science Foundation of China (61603223), Jiangsu Provincial Qinglan Project, Suzhou Science and Technology Programme (SYG202106), Research Development Fund of XJTLU (RDF-18-02-30, RDF-20-01-18), Key Program Special Fund in XJTLU (KSF-E-34) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (20KJB520034).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this work we build an end-to-end adversarial domain adaptation model for bearing fault diagnosis on two different datasets. Two different adversarial based unsupervised domain adaptation models are implemented, and the obtained results are compared and analyzed. This project proposed a novel feature extractor model structure for bearing vibration signal, and pseudo-label semi-supervised learning is applied with the implemented Maximum Classifier Discrepancy (MCD) model. The proposed method outperforms the original method on XJTU and CWRU bearing datasets and achieves 97.25% accuracy.
AB - In this work we build an end-to-end adversarial domain adaptation model for bearing fault diagnosis on two different datasets. Two different adversarial based unsupervised domain adaptation models are implemented, and the obtained results are compared and analyzed. This project proposed a novel feature extractor model structure for bearing vibration signal, and pseudo-label semi-supervised learning is applied with the implemented Maximum Classifier Discrepancy (MCD) model. The proposed method outperforms the original method on XJTU and CWRU bearing datasets and achieves 97.25% accuracy.
KW - Bearing Fault Diagnosis
KW - Pseudo-label Semi-supervised Learning
KW - Transfer Learning
KW - Transferability Estimation
KW - Unsupervised Domain Adaptation
UR - http://www.scopus.com/inward/record.url?scp=85141210562&partnerID=8YFLogxK
U2 - 10.1109/ICAC55051.2022.9911080
DO - 10.1109/ICAC55051.2022.9911080
M3 - Conference Proceeding
AN - SCOPUS:85141210562
T3 - 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022
BT - 2022 27th International Conference on Automation and Computing
A2 - Yang, Chenguang
A2 - Xu, Yuchun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Automation and Computing, ICAC 2022
Y2 - 1 September 2022 through 3 September 2022
ER -