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<p>Pipelines serve as critical infrastructure for transporting essential resources, making their integrity and reliability vital for safety and sustainability. However, deterioration over time often leads to defects such as corrosion, blockages, or weld failures, which pose serious operational and environmental risks. Traditional inline inspection (ILI) systems, though effective, face challenges in accurately detecting fine cracks, complex corrosion patterns, and stress corrosion cracking (SCC). Additionally, the vast datasets generated during inspections are difficult to interpret, requiring specialized tools and expertise. Misinterpretations or delays can result in unnecessary maintenance or overlooked critical faults, emphasizing the need for more intelligent inspection solutions. In recent years, the advancement of sensors, robotic systems, and machine learning technologies has opened new possibilities for in-pipe inspection robots (IPIRs). This study proposes the development of a Smart In-Pipe Inspection Robot (SIPIR) that integrates high-resolution sensing, robotic mobility, and advanced data analysis techniques. The SIPIR employs a YOLOv11-based machine learning framework to automatically detect and classify defects such as rust, structural cracks, blockages, and welding anomalies with high precision. The robot is designed to navigate complex, hazardous, and confined pipeline environments with minimal human intervention, enabling continuous and reliable inspection. Machine learning algorithms are applied to the collected inspection data for anomaly detection, defect classification, and predictive maintenance. Through predictive analytics, the system not only identifies existing damage but also forecasts potential risks, thereby supporting proactive decision-making. This approach reduces downtime, minimizes maintenance costs, and enhances overall operational efficiency. The integration of robotics with machine learning marks a significant advancement in pipeline inspection technology. By providing accurate, real-time insights into pipeline health, SIPIR offers a transformative solution for ensuring pipeline integrity, improving safety, and optimizing resource management.</p> Graphical abstract <p></p>

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ROS2-simulated in-pipe robotic monitoring system with machine learning based defect detection and Simulink torque analysis with GUI controlled navigation

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Prabhu Sethuramalingam,
  • R. Ambigai,
  • M. Uma,
  • Shaik Pasha,
  • Utsav Jain,
  • Arvind Sekhar,
  • Aryan Dwivedi,
  • Praneeth Kasiraju,
  • Shaik Ayman Hameed Baig,
  • Parth Kale,
  • Srinivasan Sridhar,
  • Rishabh Kothari,
  • S. S. Abilaash

摘要

Pipelines serve as critical infrastructure for transporting essential resources, making their integrity and reliability vital for safety and sustainability. However, deterioration over time often leads to defects such as corrosion, blockages, or weld failures, which pose serious operational and environmental risks. Traditional inline inspection (ILI) systems, though effective, face challenges in accurately detecting fine cracks, complex corrosion patterns, and stress corrosion cracking (SCC). Additionally, the vast datasets generated during inspections are difficult to interpret, requiring specialized tools and expertise. Misinterpretations or delays can result in unnecessary maintenance or overlooked critical faults, emphasizing the need for more intelligent inspection solutions. In recent years, the advancement of sensors, robotic systems, and machine learning technologies has opened new possibilities for in-pipe inspection robots (IPIRs). This study proposes the development of a Smart In-Pipe Inspection Robot (SIPIR) that integrates high-resolution sensing, robotic mobility, and advanced data analysis techniques. The SIPIR employs a YOLOv11-based machine learning framework to automatically detect and classify defects such as rust, structural cracks, blockages, and welding anomalies with high precision. The robot is designed to navigate complex, hazardous, and confined pipeline environments with minimal human intervention, enabling continuous and reliable inspection. Machine learning algorithms are applied to the collected inspection data for anomaly detection, defect classification, and predictive maintenance. Through predictive analytics, the system not only identifies existing damage but also forecasts potential risks, thereby supporting proactive decision-making. This approach reduces downtime, minimizes maintenance costs, and enhances overall operational efficiency. The integration of robotics with machine learning marks a significant advancement in pipeline inspection technology. By providing accurate, real-time insights into pipeline health, SIPIR offers a transformative solution for ensuring pipeline integrity, improving safety, and optimizing resource management.

Graphical abstract