Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based...

Area of Science:

• Pipeline integrity and structural health monitoring.
• Acoustic sensing technologies and data analysis.
• Mechanical defect detection in industrial infrastructure.

Background:

• Mechanical damage in pipelines poses significant risks, necessitating advanced detection methods.
• Distributed Acoustic Sensing (DAS) offers a promising approach for monitoring pipeline integrity.
• Generating realistic simulated data is crucial for training and validating detection algorithms.

Purpose of the Study:

• To develop a framework for detecting mechanical pipeline damage using simulated DAS responses.
• To create a robust dataset for classifying pipeline events such as welds, clips, and corrosion.
• To analyze the impact of sensing systems and noise on classification performance.

Main Methods:

• Simulating ultrasonic guided wave (UGW) responses and transforming them into DAS or quasi-DAS signals.
• Generating a physically robust dataset for pipeline event classification.
• Evaluating the influence of different sensor configurations and noise levels on detection accuracy.

Main Results:

• The framework successfully generates simulated DAS data for pipeline damage detection.
• Classification performance is shown to be dependent on the chosen sensing system and noise conditions.
• The study demonstrates the robustness of various sensor deployments under realistic noise levels.

Conclusions:

• The developed framework provides a reliable method for detecting mechanical pipeline damage.
• Simulated DAS data is effective for training and validating pipeline monitoring systems.
• Understanding the interplay between sensing systems and noise is critical for effective pipeline inspection.