TUBE TRADE FAIR 2024 News - Monitoring Large Pipeline Structures Using Acoustic Sensors

A team from the University of Bristol has developed and experimentally validated a method that uses mobile robots to inspect large pipeline structures. Guided by ultrasonic sensors, it examined multiple defects on a 3-meter-long steel pipe. The TUBE TRADE FAIR 2024 learned from the research report that this new technology can be used to inspect defects of different sizes such as circular holes, crack-like defects, and pits. By designing inspection paths, it achieved 100% detection coverage of defined reference defects.

In the team's published research report, the members explained the experimental principle of using a network of independent robots to inspect large plate-like structures. The inspection is divided into defect detection and defect location stages, with each robot carrying sensors capable of sending and receiving guided waves in pulse echo mode.

This approach explores the possibility of onboard processing to reduce data transmission costs and minimizes communication costs between robots without needing synchronization, thus lowering the overall inspection cost.

Acoustic sensor monitoring can also be applied to other materials, pipe geometries, noise levels, and guided wave modes. It allows for the exploration of a full range of sensor performance parameters, defect sizes and types, and operating methods. However, no related experiments have been conducted yet.

TUBE TRADE FAIR 2024 learned that the team believes that as the cost of mobile robots has decreased in recent years, deploying multiple robots for large-area inspections is no longer a pipe dream. Starting with the presence of small inspection robots, attempts are being made to apply robots to structural monitoring and explore universal detection methods.

In the future, in order to achieve this goal, it will be necessary to integrate inspection strategies, methods, and evaluation procedures with the robots. Only then can precise, low-cost, and efficient defect detection and localization truly be realized.

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