Yinglu Wang
Papers
1
Total Citations
36
H-Index
1
About
Yinglu Wang is a leading researcher in intelligent manufacturing and industrial robot fault diagnosis, with a particular focus on the reliability and health monitoring of critical robotic components. Her work addresses the pressing need for non-invasive diagnostic techniques in modern factories, where industrial robots—such as liquid crystal display transfer robots (LCDTRs)—play an essential role in high-precision production lines. Wang’s most cited study, “Fault Diagnosis of Ball Screw in Industrial Robots Using Non-Stationary Motor Current Signals” (2020, 36 citations), introduces an innovative method for detecting mechanical faults without requiring additional sensors, leveraging existing motor current signals to identify degradation in ball screws. This contribution is pivotal for reducing downtime and maintenance costs in automated manufacturing. Her research bridges signal processing, mechatronics, and condition-based maintenance, offering practical solutions for real-world industrial applications. With growing recognition in the field of prognostics and health management, Wang’s work continues to influence the development of smarter, more resilient robotic systems.
Research Focus
Key Achievements
Top Papers
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