Yung-Da Sun
Papers
1
Total Citations
35
H-Index
1
About
Yung-Da Sun is a leading researcher in intelligent fault diagnosis and deep learning for marine robotics, with a focus on propulsion system health monitoring. His most-cited work, “Multisensor Fusion Time–Frequency Analysis of Thruster Blade Fault Diagnosis Based on Deep Learning” (2022, 35 citations), pioneers a novel approach that integrates multisensor data and time-frequency analysis to detect blade damage—including healthy, fully broken, and half-broken conditions—in thrusters and propellers. This contribution addresses a critical gap in autonomous marine systems, where undetected propulsion faults can lead to catastrophic failures during sailing. By leveraging deep learning for real-time, high-accuracy diagnosis, Sun’s research enhances the reliability and safety of underwater robots. His work stands out for its practical impact on maritime operations, offering a scalable solution for predictive maintenance. With growing citations reflecting its influence, Sun is recognized for advancing the intersection of sensor fusion and artificial intelligence in marine engineering, making him a key figure in the development of robust, self-diagnosing autonomous vessels.
Research Focus
Key Achievements
Top Papers
- 1