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

Abstract

This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.

Application Research of Ultrasonic-Guided Wave Technology in Pipeline Corrosion Defect Detection: A Review

“…This limitation may be particularly significant when detecting defects in long-distance and remote areas. An alternative solution has been proposed, utilizing distributed optical fiber sensors as measurement sensors, which can be strategically placed at multiple points along the pipeline to monitor damage [138,139]. Zhang et al proposed a pipeline health monitoring method based on quasi-distributed fiber optic sensors, integrating simulated guided wave results with simulations of DAS/quasi-DAS systems.…”

“…This work exploits the spatiotemporal data matrix in a fully distributed sensor system to generate a simulated dataset, holding significant potential for applications in pipeline SHM and defect identification using DAS data. The integration of physics-based simulated datasets with DAS systems enables real-time data analysis for detecting and identifying pipeline defects, thereby significantly enhancing the accuracy of SHM [139].…”