Professor at the University of Electronic Science and Technology of China (UESTC). My research focuses on Unpiggable Pipeline In-Line Inspection, physical AI, multi-physics sensing, instrumentation and systems for nondestructive testing.
电子科技大学教授，研究方向包括非常规管道内检测、物理人工智能、多物理场感知、无损检测仪器与系统。
A traceable research platform built from real four-point-bending pull-test experiments, with frozen data indexes, quality control, Python/MATLAB workflows and explicit evidence boundaries.
基于真实四点弯牵拉实验的可追溯研究平台，包含冻结数据索引、质量控制、Python/MATLAB 程序及明确的结论边界。
- Project website / 项目主页
- Source code and documentation / 代码与文档
- P110 magnetic + strain-gauge dataset
- 406-mm MEM + remanence + eddy-current + strain dataset
- Software DOI
- Independent reproduction task / 独立复现任务
The P110 and 406-mm campaigns are kept as separate measurement contracts. The public evidence supports traceable feature/stage analysis; it does not claim a universally transferable absolute-stress model for unseen pipes without pipe-specific calibration and external validation.
- Unpiggable Pipeline In-Line Inspection (ILI) Solutions | PIGPROX https://www.pigprox.com/index.html
- 86+ Inspection Systems | 500+ Pipelines | 5,500+ km | 1.2M+ Defects Detected | Φ108–Φ711 mm | CNPC & Sinopec | 1.5D bend
- Selected publications / 代表性论文
- Related electromagnetic and pipeline-inspection papers
- PIGPROX product materials
Open to independent reproduction, physically grounded method proposals and research collaboration.