CV

🔥Parallel Relation Network for Intelligent Fault Detection and Localization of Train Transmission Systems with Zero-fault Sample
⭐First author and delivered an oral presentation to IEEE PHM 2024
TL;DR: A PRN model combining RSN and KAN is proposed for train transmission fault detection with few samples, achieving over 98% accuracy and strong generalization.

🔥Pseudo-fault data enhanced relation network for fault detection and localization in train transmission systems
⭐First author published in EAAI
TL;DR: A relation network using pseudo-fault data improves train transmission fault detection and localization performance.

🔥Decoupling Intrinsic Fault Features from Domain Variations via Domain-Attribute Fusion for Unseen-Domain Fault Diagnosis
⭐First author submitted manuscript to MSSP
TL;DR: A domain-attribute fusion model decouples intrinsic fault features, improving unseen-domain diagnosis robustness.

🔥Unified Health Domain Relation Learning for Train Transmission Systems Fault Detection under Complex Operating Conditions
⭐Second author (first author of adviser) submitted manuscript to IEEE TII
TL;DR: A unified health-domain relation learning approach enhances fault detection under complex operating conditions.

🔥Open-Set Fault Diagnosis Using CLIP with Forward-Reverse Reasoning
⭐Second author (first author of adviser) submitted manuscript to COMPUT IND
TL;DR: A CLIP-based forward–reverse reasoning model enable for fault diagnosis

Publications & Patents

Published

Unified Health Domain Relation Learning for Train Transmission Systems Fault Detection under Complex Operating Conditions

Z. Chen*, Z. Duan*, and H.-Z. Huang, "Unified Health Domain Relation Learning for Train Transmission Systems Fault Detection under Complex Operating Conditions," Structural Health Monitoring, 2026, doi: https://doi.org/10.1177/1475921720961234.

Pseudo-fault data enhanced relation network for fault detection and localization in train transmission systems

Z. Duan, R. Liu, Z. Chen, and H.-Z. Huang, "Pseudo-fault data enhanced relation network for fault detection and localization in train transmission systems," Engineering Applications of Artificial Intelligence, vol. 158, p. 111515, 2025/10/15/ 2025, doi: https://doi.org/10.1016/j.engappai.2025.111515.

Parallel Relation Network for Intelligent Fault Detection and Localization of Train Transmission Systems with Zero-fault Sample

Z. Duan, R. Liu, Z. Chen and H. -Z. Huang, "Parallel Relation Network for Intelligent Fault Detection and Localization of Train Transmission Systems with Zero-fault Sample," 2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing), Beijing, China, 2024, pp. 1-6.

Unpublished

• Second author (first author of adviser) of a submitted manuscript to the SCI Q1 journal MSSP (Mechanical Systems and Signal Processing)
• Second author (first author of adviser) of a submitted manuscript to the SCI Q1 journal IEEE TII (Transactions on Industrial Informatics)

Patents

• Second author of an invention patent (first author of adviser), accepted for filing.

Skills

• Python, Pytorch, Matlab, C, git, Linux
• Solidworks
• Power Point, Visio, OriginLab

Leadership

Vice President and founding core member of UESTC Interdisciplinary Association (IA).

• Invited Professor Dezhong Yao (Founding Dean of UESTC’s School of Life Science and Technology) and brain scientist Professor Pedro Antonio Valdes Sosa as advisors
• to promote the society’s outreach and research activities in fields such as bioinformatics, bioimaging, bioelectronics, and AI for Life Science.
• Responsible for building and maintaining the association’s official website.

Student Mentor for SMEE 2025 Freshmen

• Mentored freshmen under the guidance of Counselor Keyu Chen