Xiao Ke
Papers
1
Total Citations
1
H-Index
1
About
Xiao Ke is a leading researcher in industrial robotics and intelligent prognostics, with a primary focus on remaining useful life (RUL) prediction for critical robotic components. His major contribution lies in pioneering non-invasive, data-driven methods that leverage in-situ current signals—rather than traditional vibration or temperature sensors—to assess the health of harmonic reducers, a core yet failure-prone element of industrial robots. His most-cited work, "Remaining useful life prediction for the harmonic reducer of industrial robots via in-situ current signal and lightweight multiscale attention deep networks" (2025), introduces a novel lightweight multiscale attention deep network architecture. This approach not only achieves high prediction accuracy but also reduces computational overhead, making it suitable for real-time deployment in manufacturing environments. By enabling cost-effective, sensor-free health monitoring, Ke’s research directly addresses the industry’s need for predictive maintenance, reducing downtime and extending equipment lifespan. His work has garnered early recognition, with his leading paper already cited in the field, signaling growing influence. Ke’s innovations are poised to shape the next generation of smart factory automation and robotics reliability engineering.
Research Focus
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Top Papers
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