Li, Jun https://orcid.org/0000-0002-9834-667X
Liu, Yongbao
Li, Qijie https://orcid.org/0000-0001-7840-1750
Funding for this research was provided by:
National Natural Science Foundation of China (51725902 and 41890820)
the Royal Academy of Engineering through the Urban Flooding Research Policy Impact Programme (UUFRIP\100031)
the Natural Science Independent Project of Naval University of Engineering (425317K004 and 425317K137)
the Newton Advanced Fellowships from the NSFC and the UK Royal Society (52061130219 and NAF\R1\201156)
Article Title: Generative adversarial network and transfer-learning-based fault detection for rotating machinery with imbalanced data condition
Journal Title: Measurement Science and Technology
Article Type: paper
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Publication dates
Date Received: 2021-08-31
Date Accepted: 2021-11-12
Online publication date: 2022-01-11