Xingkai Yang
Papers
1
Total Citations
11
H-Index
1
About
Xingkai Yang is a leading researcher in intelligent fault detection and diagnostics for rotating machinery, with a focus on signal processing and vibration analysis. His most-cited work, "Normalizing vibration signals with a novel piecewise power fitting method for intelligent fault detection of rotating machinery" (2024), has already garnered 11 citations, underscoring its timely impact on the field. Yang’s major contribution lies in developing innovative normalization techniques that enhance the accuracy and reliability of machine learning models for fault diagnosis, addressing critical challenges in industrial predictive maintenance. By introducing a piecewise power fitting approach, he has advanced the ability to process complex, non-stationary vibration signals, enabling more robust detection of early-stage mechanical failures. This work is particularly notable for its practical applicability in real-world rotating machinery, such as turbines and gearboxes, where signal variability often complicates analysis. Yang’s research bridges the gap between theoretical signal processing and engineering applications, offering scalable solutions for Industry 4.0. His growing citation record reflects the relevance of his methods to both academic researchers and industrial practitioners seeking to improve equipment reliability and reduce downtime.
Research Focus
Key Achievements
Top Papers
- 1