School of Mechanical and Electrical Engineering, UESTCI am a B.S. student at the School of Mechanical and Electrical Engineering (SMEE), University of Electronic Science and Technology of China (UESTC). I am a Research Assistant at ReliaLab (Center for System Reliability and Safety, China), advised by Zuoyi Chen and Prof. Hong-Zhong Huang. Also a Research Assistant at EPICLab, SAI of SJTU, advised by Linfeng Zhang.
My research interests focus on AI4Reliability including zero/few-shot learning, transfer learning, and LLM.
Education
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University of Electronic Science and Technology of ChinaSchool of Mechanical and Electrical Engineering
B.S. StudentSep. 2023 - Jul. 2027
Experience
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Shanghai Jiaotong UniversityResearch InternNov. 2025 - Present -
UESTC Interdisciplinary AssociationVice PresidentNov. 2025 - Present
Honors & Awards
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National Scholarship2025
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National Scholarship2024
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Ranked 1/83 in major2024
News
Talk/Report
Selected Publications (view all )
Unified Health Domain Relation Learning for Train Transmission Systems Fault Detection under Complex Operating Conditions
Z. Chen*, Zhixu Duan*, H.-Z. Huang (* equal contribution)
Structural Health Monitoring (SHM, CAS Q2) 2026
A unified health-domain relation learning approach enhances fault detection under complex operating conditions.
Unified Health Domain Relation Learning for Train Transmission Systems Fault Detection under Complex Operating Conditions
Z. Chen*, Zhixu Duan*, H.-Z. Huang (* equal contribution)
Structural Health Monitoring (SHM, CAS Q2) 2026
A unified health-domain relation learning approach enhances fault detection under complex operating conditions.
Decoupling Intrinsic Fault Features from Domain Variations via Domain-Attribute Fusion for Unseen-Domain Fault Diagnosis
Zhixu Duan, et al.
Submitted to Advanced Engineering Informatics (AEI, CAS Q1) 2026 Minor Revision
A domain-attribute fusion model decouples intrinsic fault features, improving unseen-domain diagnosis robustness.
Decoupling Intrinsic Fault Features from Domain Variations via Domain-Attribute Fusion for Unseen-Domain Fault Diagnosis
Zhixu Duan, et al.
Submitted to Advanced Engineering Informatics (AEI, CAS Q1) 2026 Minor Revision
A domain-attribute fusion model decouples intrinsic fault features, improving unseen-domain diagnosis robustness.
Collaborative Teacher-Student Learning: Simulated Domain Attacks for Class-Intrinsic Feature Learning in Multi-Domain Generalized Fault Diagnosis
Zhixu Duan, et al.
Submitted to IEEE Transactions on Industrial Informatics (IEEE TII, CAS Q1)Under Review. 2026
A collaborative teacher-student learning framework enhances multi-domain generalized fault diagnosis.
Collaborative Teacher-Student Learning: Simulated Domain Attacks for Class-Intrinsic Feature Learning in Multi-Domain Generalized Fault Diagnosis
Zhixu Duan, et al.
Submitted to IEEE Transactions on Industrial Informatics (IEEE TII, CAS Q1)Under Review. 2026
A collaborative teacher-student learning framework enhances multi-domain generalized fault diagnosis.
Open-Set Fault Diagnosis Using CLIP with Forward-Reverse Reasoning
Z. Chen*, Zhixu Duan*, H.-Z. Huang (* equal contribution)
Submitted to Computers in Industry (COMPUT IND, CAS Q1) 2026 Minor Revision
A CLIP-based forward-reverse reasoning model enable for fault diagnosis.
Open-Set Fault Diagnosis Using CLIP with Forward-Reverse Reasoning
Z. Chen*, Zhixu Duan*, H.-Z. Huang (* equal contribution)
Submitted to Computers in Industry (COMPUT IND, CAS Q1) 2026 Minor Revision
A CLIP-based forward-reverse reasoning model enable for fault diagnosis.
Pseudo-fault data enhanced relation network for fault detection and localization in train transmission systems
Zhixu Duan, R. Liu, Z. Chen, H.-Z. Huang
Engineering Applications of Artificial Intelligence (EAAI, CAS Q1) 2025
A relation network using pseudo-fault data improves train transmission fault detection and localization performance.
Pseudo-fault data enhanced relation network for fault detection and localization in train transmission systems
Zhixu Duan, R. Liu, Z. Chen, H.-Z. Huang
Engineering Applications of Artificial Intelligence (EAAI, CAS Q1) 2025
A relation network using pseudo-fault data improves train transmission fault detection and localization performance.
Parallel Relation Network for Intelligent Fault Detection and Localization of Train Transmission Systems with Zero-fault Sample
Zhixu Duan, R. Liu, Z. Chen, H.-Z. Huang
IEEE PHM 2024 2024 Oral Presentation
A PRN model combining RSN and KAN is proposed for train transmission fault detection with few samples, achieving over 98% accuracy.
Parallel Relation Network for Intelligent Fault Detection and Localization of Train Transmission Systems with Zero-fault Sample
Zhixu Duan, R. Liu, Z. Chen, H.-Z. Huang
IEEE PHM 2024 2024 Oral Presentation
A PRN model combining RSN and KAN is proposed for train transmission fault detection with few samples, achieving over 98% accuracy.