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Published in IEEE Transactions on Industrial Electronics, 2023
This paper uses a relatively traditional method for fault mechanism analysis and manual feature design.
Recommended citation: Conggan Ma, Jiaming Li*, Nic Zhang, Feifei Bu, Zhixin Yang. Open-circuit radial stray magnetic flux density based noninvasive diagnosis for mixed eccentricity parameters of interior permanent magnet synchronous motors in electric vehicles. IEEE Transactions on Industrial Electronics. 2023, 70(2): 1983-1992.
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Published in Mechanical Systems and Signal Processing, 2025
This paper proposes a novel dataset and checks the relability of data itself.
Recommended citation: Jiaming Li, Hao Chen, Xian-Bo Wang, Zhi-Xin Yang*. A comprehensive gear eccentricity dataset with multiple fault severity levels: Description, characteristics analysis, and fault diagnosis applications. Mechanical Systems and Signal Processing. 2025, 224: 112068.
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Published in IEEE Transactions on Industrial Informatics, 2025
This paper integrates physical kowledge into deep-learning models for gear fault severity levels classification with better intepretability.
Recommended citation: Jiaming Li, Xian-Bo Wang, Hao Chen, Zhi-Xin Yang*. Physical-knowledge-guided and interpretable deep neural networks for gear fault severity level diagnosis. IEEE Transactions on Industrial Informatics. 2025, 21(6): 4892-4903.
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Published in Mechanical Systems and Signal Processing, 2026
This paper reviews knowledge integration in intelligent fault diagnosis for better interpretability and generalizability.
Recommended citation: Jiaming Li, Chenhui Zheng, Zhiwei Wu, Hao Chen, Xian-Bo Wang, Zhi-Xin Yang*. Review of fault diagnosis for rotating machinery: Prior knowledge integration in data-driven methods benefits model interpretability and generalizability. Mechanical Systems and Signal Processing. 2026, 242: 113623.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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