Papers
1
Total Citations
14
H-Index
1
About
Dr. Zhijian Tu is a researcher specializing in mechanical fault diagnosis, signal processing, and intelligent condition monitoring of rotating machinery. His most-cited work, "Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM" (2023, 14 citations), introduces a novel hybrid approach that combines Ensemble Empirical Mode Decomposition (EEMD) with Marine Predators Algorithm (MPA) and Kernel Extreme Learning Machine (KELM) to accurately assess the working state of RV reducers. This model addresses the challenge of irregular rotation periods in aging machinery, enabling more reliable fault detection in industrial applications. Dr. Tu’s contributions advance the field of predictive maintenance by integrating adaptive signal decomposition with optimized machine learning, offering a robust framework for evaluating torque transfer and periodic behavior in complex mechanical systems. His work has practical significance for improving the reliability and longevity of rotating equipment in manufacturing and robotics. With growing interest in intelligent fault diagnosis, Dr. Tu’s research continues to influence both academic studies and industrial practices in condition-based monitoring.
Research Focus
Key Achievements
Top Papers
- 1Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM14 citations · 2023