Mingyuan Yang
Papers
1
Total Citations
6
H-Index
1
About
Mingyuan Yang is a researcher in mechanical engineering and fault diagnostics, with a primary focus on predictive maintenance and signal processing for rotating machinery. Their most cited work, "A fault prediction method for CMOR bearings based on parameter-optimized variational mode decomposition and autocorrelation function" (2025, 6 citations), introduces a novel approach to bearing fault detection by combining variational mode decomposition with autocorrelation analysis. This method enhances the accuracy of early fault identification in critical industrial components, addressing a key challenge in condition monitoring. Yang’s contributions lie in optimizing signal decomposition parameters to improve the reliability of fault prediction, reducing downtime in manufacturing and energy systems. While their citation count is still growing, the work demonstrates practical impact by offering a computationally efficient solution for real-world bearing diagnostics. Yang’s research bridges theoretical signal processing and applied mechanical systems, making it valuable for engineers and researchers working on predictive maintenance. Their focus on parameter optimization and autocorrelation-based feature extraction marks a step forward in non-invasive fault detection, with potential applications in wind turbines, automotive systems, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1