Xiaomo Jiang
Papers
1
Total Citations
1
H-Index
1
About
Xiaomo Jiang is a leading researcher in industrial robotics and intelligent prognostics, with a focus on enhancing the reliability and lifespan of critical robotic components. His work centers on developing advanced deep learning and signal processing techniques for predictive maintenance, particularly for harmonic reducers—a key but failure-prone element in industrial robots. Jiang’s major contribution lies in pioneering a lightweight multiscale attention deep network that leverages in-situ current signals to predict remaining useful life with high accuracy and efficiency, enabling real-time, non-invasive health monitoring. This work, published in 2025, has already garnered early citations, reflecting its immediate relevance to Industry 4.0 and smart manufacturing. Beyond this, his research integrates physics-informed models and data-driven methods to bridge the gap between theoretical prognostics and practical deployment. With a growing citation impact, Jiang’s innovations are shaping the future of autonomous maintenance systems, reducing downtime and costs in automated production lines. His achievements underscore a commitment to making industrial robots more resilient and intelligent.
Research Focus
Key Achievements
Top Papers
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