@inproceedings{a6862a46479e4756b340496296a1ce70,
title = "Rolling Bearing Fault Diagnosis Based on Deep Adversarial Networks with Convolutional Layer and Wasserstein Distance",
abstract = "Intelligent bearing fault diagnosis techniques have been well developed to meet the economy and safety criteria. Machine learning and deep learning schemes have shown to be promising tools for rolling bearing defect diagnosis. They require multitudinous labelled data in the training phase and assume that the training and testing samples abide by the same data distribution. However, in real-world industrial contexts, these two preconditions are almost impossible to be satisfied. Conversely, approaches based on transfer learning are potent instruments for proactively reacting to the above two challenges. Consequently, this paper presents an unsupervised method for diagnosing rolling bearing defects based on transfer learning. Convolutional neural networks, adversarial networks, and Wasserstein distance are adopted to extract domain invariant features, narrow the discrepancy between the source domain and target domain, and precisely categorize the faulty samples. A series of experiments corroborate that the proposed model can effectively facilitate the overall performance and outperform several traditional approaches under six measurement metrics.",
keywords = "fault diagnosis, neural network, rotating machinery, transfer learning",
author = "Xinyu Gao and Rui Yang and Lim, {Eng Gee}",
note = "Funding Information: This work is partially supported by the National Natural Science Foundation of China (61603223), the Jiangsu Provincial Qinglan Project, the Suzhou Science and Technology Programme (SYG202106), the Research Development Fund of XJTLU (RDF-18-02-30, RDF-20-01-18), the Key Program Special Fund in XJTLU (KSF-E-34) and The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (20KJB520034). Publisher Copyright: {\textcopyright} 2022 IEEE.; 27th International Conference on Automation and Computing, ICAC 2022 ; Conference date: 01-09-2022 Through 03-09-2022",
year = "2022",
doi = "10.1109/ICAC55051.2022.9911134",
language = "English",
series = "2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Chenguang Yang and Yuchun Xu",
booktitle = "2022 27th International Conference on Automation and Computing",
}