Xinghua Wang
Papers
2
Total Citations
76
H-Index
2
About
Xinghua Wang is a leading researcher in advanced industrial robotics and precision motion control, with a focus on enhancing the performance and reliability of servo systems. His major contributions lie at the intersection of high-precision motor control and intelligent fault diagnosis. In his highly cited 2019 work (55 citations), Wang introduced a novel direct torque control (NDTC) scheme for permanent magnet synchronous motors (PMSMs) in industrial robots, utilizing composite active vectors to dramatically reduce torque ripple and improve servo accuracy—a critical advancement for high-stakes automation. Complementing this, his 2020 study (21 citations) pioneered a multi-sensor information fusion approach combined with 1D convolutional neural networks for real-time fault diagnosis of industrial robots. This work enables condition-based maintenance, significantly extending system lifespan and reducing downtime. Wang’s research seamlessly bridges control theory and deep learning, offering practical solutions for robust, long-term operation of industrial servo systems. His achievements are pivotal for the next generation of smart manufacturing and autonomous robotics.
Research Focus
Key Achievements
Top Papers
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