Papers
7
Total Citations
57
H-Index
3
About
Yunfeng Xia is a researcher whose work centers on power transmission line inspection, fault detection, and intelligent diagnostic systems. His most significant contributions lie in developing advanced non-destructive testing methods for identifying broken strands in overhead transmission lines — a critical safety concern given the harsh environmental conditions these structures endure, including lightning strikes, chemical corrosion, ice-shedding, and wind vibration. Xia's most impactful work combines signal processing with machine learning, notably his 2011 paper applying S-Transform and Support Vector Machine (SVM) techniques for real-time broken strand diagnosis, which has garnered 22 citations. Complementing this, he pioneered smart eddy current transducers carried by inspection robots and optimized Magnetic Flux Leakage (MFL) sensors for detecting faults in aluminum conductor steel-reinforced (ACSR) cables, earning 13 and 12 citations respectively. These contributions collectively represent a comprehensive inspection framework spanning sensor design, robot-assisted deployment, and quantitative fault identification. More recently, Xia has expanded his research into mobile robotics, proposing an Extreme Learning Machine-optimized A* algorithm for path planning. With a career spanning over a decade, his interdisciplinary approach — merging electromagnetics, machine learning, and robotics — makes him a notable contributor to intelligent infrastructure monitoring systems.
Research Focus
Key Achievements
Top Papers
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- 4A LMF based broken strand faults detection scheme for steel core in ACSR3 citations · 2010
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- 6Path Planning Method Based on Multi-Layer ELM Optimized A*2 citations · 2021
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