@inproceedings{78689821c7ef44b49efbfeaf20abbc11,
title = "A Novel Residual Domain Adaptation Network for Intelligent Transfer Diagnosis",
abstract = "Deep neural networks based intelligent diagnosis methods are able to learn powerful features for accurate fault classification, however they cannot always generalize well across changes in data distributions. To address this issue, a novel residual domain adaptation network is proposed for transfer diagnosis of machinery in this paper. In the proposed framework, one-dimensional residual network is designed as the feature generator, then a mixed moment matching strategy, including first-order statistics and second-order statistics, is proposed to minimize the distribution discrepancy across domains. The comprehensive experiments on rolling bearing fault dataset are constructed to evaluate the proposed method. The results show the effectiveness of the proposed method.",
keywords = "Domain adaptation, Intelligent diagnosis, Residual network, Transfer learning",
author = "Jinyang Jiao and Ming Zhao and Jing Lin and Kaixuan Liang and Chuancang Ding",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2019 ; Conference date: 03-09-2019 Through 05-09-2019",
year = "2020",
doi = "10.1007/978-3-030-57745-2\_68",
language = "英语",
isbn = "9783030577445",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "827--839",
editor = "Andrew Ball and Len Gelman and B.K.N. Rao",
booktitle = "Advances in Asset Management and Condition Monitoring, COMADEM 2019",
}