DEVELOPMENT AND RESEARCH OF IN-PIPE DEFECTS DETECTION AND INSPECTION SYSTEM

Pipeline integrity and safety are critical for transporting water, oil and gas, but traditional inspection methods are resource-intensive and error-prone, delaying defect detection. The objective of this work is to create an autonomous robotic system capable of detecting, localizing and classifying problems inside a pipe using sophisticated imaging techniques and artificial intelligence algorithms. To achieve this goal, multimodal precision-enhancing sensors (high-resolution RGB cameras, ultrasonic and infrared sensors) with data processing methods such as the Canny edge detector and the DBSCAN clustering algorithm were used. The research approaches include the creation of a modular robotic platform for autonomous navigation, the creation of synthetic data for training deep neural networks, and experimental validation on pipelines of different materials and dimensions. The experimental results show that the system outperforms existing approaches, making it a valuable tool for predictive maintenance, regulatory compliance and improving pipeline safety.

1. Smart Pipe Inspection Robot With In-Chassis Motor Actuation Design and Integrated AI-Powered Defect Detection System, D. Zholtayev et al // in IEEE Access. – 2024. – Vol. 12. – Р. 119520-119534. https://ieeexplore.ieee.org/document/10649586.

2. Design and Development of an In-Pipe Mobile Robot for Pipeline Inspection with AI Defect Detection System / A. Kenzhekhan et al // 23rd International Conference on Control, Automation and Systems (ICCAS), Yeosu, Korea, Republic of. – 2023. – Р. 579-584. https://doi.org/10.23919/ICCAS59377.2023.10316817.

3. Development of an In-Pipe Inspection Robot for Large-Diameter Water Pipes / K-W. Jeon et al // Sensors. – 2024. – № 24(11). – Р. 3470. https://doi.org/10.3390/s24113470.

4. Autonomous Navigation of In-Pipe Inspection Robot Using Contact Sensor Modules / H. Jang et al // in IEEE/ASME Transactions on Mechatronics. – 2022. – Vol. 27. – № 6. – Р. 4665-4674. https://doi.org/10.1109/TMECH.2022.3162192 https://ieeexplore.ieee.org/document/9759493.

5. Defect detection in pipe structures using stochastic resonance of Duffing oscillator and ultrasonic guided waves / Wu Jing et al // International Journal of Pressure Vessels and Piping. – 2020. – № 187. – Р. 104168. https://doi.org/10.1016/j.ymssp.2016.05.012.

6. Automatic Detection of Water Supply Pipe Defects Based on Underwater Image Enhancement and Improved YOLOX / Changwang Su et al // Journal of Construction Engineering and Management. – 2024. – № 150. – Р. 04024134. https://doi.org/10.1061/JCEMD4.COENG-14919.

7. Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis / J. Huang et al // Sensors. – 2024. – № 24(9). – Р. 2699. https://doi.org/10.3390/s24092699.

8. Experimental investigation for multi-defect detection of industrial pipeline based on passive infrared thermal imaging analysis: A case study /Zhou Gang et al // International Journal of Pressure Vessels and Piping. – 2023. – № 206. – Р. 105058. https://www.sciencedirect.com/science/article/pii/S0308016123001758.

9. A Rapid Screening Method for Suspected Defects in Steel Pipe Welds by Combining Correspondence Mechanism and Normalizing Flow / W. Cui et al // in IEEE Transactions on Industrial Informatics. – 2024. – Vol. 20, № 9. – Р. 11171-11180. https://doi.org/10.1109/TII.2024.3399934 https://ieeexplore.ieee.org/document/10538047

10. Mustafaev B. Еnhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation / B. Mustafaev, S. Kim, E. Kim // in IEEE Access. – 2023. – Vol. 11. – Р. 113339-113359. https://doi.org/10.1109/ACCESS.2023.3322734 https://ieeexplore.ieee.org/document/10274090.