Jialing Wu
Papers
1
Total Citations
3
H-Index
1
About
Dr. Jialing Wu specializes in intelligent fault diagnosis and industrial defect detection, with a focus on hydropower infrastructure. Her most-cited work introduces a mobile robot-based method for detecting surface defects on large hydraulic turbine runner blades, employing an improved real-time lightweight network to identify cavitation erosion, wear, and thermal stress cracks. This research addresses a critical challenge in hydropower unit maintenance, enabling safer and more efficient inspections in confined foundation pits. While her citation count is currently modest, her contributions are notable for their practical engineering impact, bridging computer vision and renewable energy asset management. Dr. Wu’s work exemplifies the application of deep learning to real-world industrial problems, offering a scalable solution for early defect detection that can prevent catastrophic failures. Her research holds promise for advancing predictive maintenance in hydropower, contributing to the reliability and longevity of clean energy infrastructure.
Research Focus
Key Achievements
Top Papers
- 1