Jing Ou
Papers
1
Total Citations
26
H-Index
1
About
Jing Ou is a researcher specializing in intelligent condition monitoring and industrial image analysis, with a particular focus on mechanical systems and structural health. Their most notable contribution is the development of a U-Net-based intelligent approach for belt morphology quantification and wear monitoring, published in 2022, which has already garnered 26 citations. This work demonstrates a novel application of deep learning to precisely measure and track the degradation of industrial belts, offering a non-invasive, automated solution for predictive maintenance. By integrating computer vision with mechanical engineering, Ou’s research enables real-time, high-accuracy assessment of surface wear patterns, significantly improving the reliability and safety of conveyor systems and other belt-driven machinery. Their approach not only reduces manual inspection costs but also enhances early fault detection, a critical advancement for industries reliant on continuous operation. With a growing citation record, Jing Ou’s work is establishing a foundation for data-driven maintenance strategies, bridging the gap between artificial intelligence and practical industrial diagnostics.
Research Focus
Key Achievements
Top Papers
- 1