Papers
2
Total Citations
191
H-Index
2
About
Dr. Qing Ni is a leading researcher in intelligent manufacturing and mechanical systems health management, with a focus on gearbox prognostics and wear monitoring. Her work addresses critical challenges in machine tools and robotics, where gear surface wear can lead to catastrophic failures. Dr. Ni’s most influential contributions include developing a novel vibration-based prognostic scheme for gear health management, which tracks surface wear progression in intelligent manufacturing systems—a paper that has garnered 136 citations. She also pioneered a cyclostationarity-based wear monitoring framework for spur gears, cited 55 times, which leverages advanced signal processing to detect early-stage gear degradation in harsh industrial environments. These frameworks enable predictive maintenance, reducing downtime and extending equipment lifespan. Dr. Ni’s research bridges the gap between theoretical signal analysis and practical manufacturing applications, making her work essential for engineers and researchers in condition-based maintenance. Her achievements highlight her as a key figure in advancing smart manufacturing reliability, with her methodologies widely adopted in both academia and industry for gear health assessment.
Research Focus
Key Achievements
Top Papers
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