Yongming Liu

Anhui Polytechnic University, Arizona State University

Papers

Total Citations

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About

Yongming Liu is a researcher at the forefront of intelligent fault diagnosis and human-robot collaboration for critical infrastructure. His work primarily focuses on two key areas: developing advanced signal processing and machine learning models for rotating machinery health monitoring, and designing collaborative robotic systems for industrial inspection tasks. In his highly cited 2023 paper, Liu introduced a novel Rotate Vector (RV) reducer fault diagnosis model that integrates Ensemble Empirical Mode Decomposition (EEMD) with a Marine Predators Algorithm-optimized Kernel Extreme Learning Machine (MPA-KELM), achieving 14 citations for its ability to accurately assess the working state of rotating machinery under irregular periodic conditions. Complementing this technical contribution, Liu’s collaborative design study on situating robots within the organizational dynamics of the gas energy industry (5 citations) demonstrates his commitment to bridging human-robot interaction (HRI) theory with real-world energy transportation needs, specifically for gas pipeline inspection. By combining robust algorithmic innovation with user-centered design, Liu’s work directly supports the reliability and safety of essential energy infrastructure, making him a notable figure in applied intelligent systems and industrial robotics.