🔥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</p>
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 to 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
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.
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.