Minqiang Xu

Papers

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About

Minqiang Xu is a leading researcher in mechanical fault diagnosis and intelligent maintenance, with a particular focus on the reliability of harmonic reducers—critical components in industrial robotics and precision machinery. His most-cited work, "A fault diagnosis scheme for harmonic reducer under practical operating conditions" (2024), has already garnered 20 citations, reflecting its immediate impact on the field. Xu’s major contribution lies in developing robust diagnostic frameworks that operate effectively under real-world, non-ideal conditions, bridging the gap between theoretical models and industrial application. By integrating signal processing, machine learning, and domain-specific mechanical knowledge, he has advanced the ability to detect early-stage faults in complex transmission systems, thereby improving operational safety and reducing downtime. His research is particularly notable for its practical orientation, addressing challenges such as variable loads, noise, and limited labeled data that plague traditional diagnostic methods. Xu’s work is essential reading for engineers and researchers in prognostics and health management, offering scalable solutions for smart manufacturing and Industry 4.0. With a growing citation record and a focus on translational research, he is establishing himself as a key voice in the next generation of condition monitoring.