Weigang Wen
Papers
1
Total Citations
9
H-Index
1
About
Weigang Wen is a researcher focused on advancing the condition monitoring and fault diagnosis of rotating machinery and robotic systems. His work addresses the critical challenge of separating vibration source signals in increasingly complex mechanical environments, a key step toward ensuring the reliability and safety of automated industrial equipment. His most-cited paper, “Vibration Source Signal Separation of Rotating Machinery Equipment and Robot Bearings Based on Low Rank Constraint” (2021, 9 citations), introduces a novel low-rank constraint approach to disentangle overlapping vibration signals. This contribution is particularly significant for the condition monitoring of robot bearings and other rotating components, where traditional methods often struggle with signal interference. By improving the accuracy of source separation, Wen’s research directly supports the development of smarter, more autonomous maintenance systems. His work is foundational for researchers and engineers working on predictive maintenance and the next generation of intelligent industrial robots.
Research Focus
Key Achievements
Top Papers
- 1