Jiaming Li

Jiaming Li

Machinery fault diagnosis, PHM, Explainable AI, Fault simulation and experimental validation

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

A Traditional Fault Diagnosis Method

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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A Dataset Study

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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An Intelligent Fault Diagnosis Method

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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A Review

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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talks

UNIfied 2026-SMMI

Published:

Master student Hao Yang contributed a conference paper and gave an oral presentation.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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