Jiaming Li

Jiaming Li

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

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

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.
Download Paper

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.
Download Paper

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.
Download Paper

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.
Download Paper