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Gas pipelines are critical for transporting vast quantities of natural gas across regions. Third-party damage, such as unauthorized excavation activities is a primary cause of accidents and damage to these pipelines, leading to significant economic losses, environmental harm, and potential threats to human safety. The importance of detecting third-party damages as early as possible cannot be overstated, as it allows pipeline operators to take timely actions to prevent damage. Recent technological advancements, particularly the development of fiber optic sensors, offer promising solutions for real-time monitoring and early warning systems against such damages. Analyzing incidents related to third-party damages present significant challenges due to their non-adherence to physical laws, in contrast to phenomena like corrosion, temperature, and pressure changes. Traditional analytical or empirical models fall short of detecting such damages effectively. However, deep learning techniques have demonstrated notable success in identifying distinctive features from non-physical data sources, including images, speech, and third-party damage acoustic signals pertinent to this study. The efficacy of deep learning methods is contingent upon the availability of a robust dataset for training. The scarcity of fiber optic sensor data pertaining to third-party damages is a critical limitation in this field. This research aims to mitigate this challenge by generating a dataset of third-party damage events on a laboratory scale utilizing a single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional neural networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.
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