Papers
8
Total Citations
130
H-Index
5
About
Zhuowei Wang is a leading researcher at the intersection of artificial intelligence and industrial robotics, specializing in knowledge graph construction, fault diagnosis, and intelligent control systems. Their most significant contribution is pioneering the use of large language models (LLMs) to build fine-grained knowledge graphs for robotic fault diagnosis, as demonstrated in their highly cited 2025 paper (58 citations). Wang’s foundational work on event logic knowledge graph construction for robot transmission system fault diagnosis (2022, 46 citations) established a systematic framework for organizing maintenance logs into actionable diagnostic knowledge. Their research also addresses critical challenges in deep learning-based fault diagnosis, including limited labeled data through graph fusion and propagation techniques, and spatio-temporal modeling with graph neural networks. Beyond diagnostics, Wang has contributed to hardware innovation, including a power management system for self-reconfigurable multi-module systems and a dust-resistant docking mechanism for surface exploration robots. Their recent work on collaborative LLM agents for flexible software development in industrial robot control systems (2025) represents a forward-looking approach to reducing development barriers. With a growing citation impact and continuous publication in top venues, Wang is shaping the future of intelligent, data-driven industrial robotics.
Research Focus
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