Papers
13
Total Citations
107
H-Index
6
About
Fengshou Gu is a distinguished researcher specializing in condition monitoring, fault diagnosis, and intelligent maintenance systems for industrial machinery. His work spans vibration analysis, acoustic sensing, and artificial intelligence-driven diagnostics, with a particular focus on ensuring the reliability and safety of complex engineering systems. Gu's most significant contributions include pioneering the application of few-shot learning approaches to fault diagnosis using vibration data — a rapidly growing area that addresses the chronic scarcity of labeled fault data in industrial settings, earning 35 citations since 2023. His development of Modulation Signal Bispectrum Enhanced Squared Envelope techniques has advanced compound epicyclic gear fault detection, accumulating 22 citations and offering practical solutions for aerospace, automotive, and wind turbine industries. More recently, Gu has emerged as a leading voice in collaborative robot health management, developing hybrid digital twin schemes and AI-based anomaly detection frameworks for smart manufacturing environments. His innovative integration of mobile robotics with acoustic source localization represents a forward-thinking approach to scalable industrial inspection. Collectively, his portfolio reflects both technical depth and strong translational impact, making his research essential reading for engineers and students working at the intersection of mechanical diagnostics, robotics, and machine learning.
Research Focus
Key Achievements
Top Papers
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- 6Vibration Analysis Based Condition Monitoring for Industrial Robots6 citations · 2021
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