AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

• DOI: 10.1609/aaai.v38i20.30264
• Publication Date: 2024-03-25T12:33:09Z
• Authors: Chengyuan Zhu; Yiyuan Yang; Kaixiang Yang; Haifeng Zhang; Qinmin Yang; C. L. Philip Chen
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0211 other engineering and technologies; 02 engineering and technology; Machine Learning (cs.LG)

The application of artificial intelligence technology has greatly enhanced and fortified the safety energy pipelines, particularly in safeguarding against external threats. predominant methods involve integration intelligent sensors to detect vibration, enabling identification event types locations, thereby replacing manual detection methods. However, practical implementation exposed a limitation current - their constrained ability accurately discern spatial dimensions signals, which complicates authentication threat events. Our research endeavors overcome above issues by harnessing deep learning techniques achieve more fine-grained recognition localization process. This refinement is crucial effectively identifying genuine threats thus enhancing transportation. paper proposes radial estimation method for pipelines based on distributed optical fiber sensing technology. Specifically, we introduce continuous multi-view multi-domain feature fusion methodology extract comprehensive signal features construct network. utilization collected acoustic data optimized, underlying principle elucidated. Moreover, incorporate concept transfer through pre-trained model, both accuracy training efficiency. Empirical evidence gathered from real-world scenarios underscores efficacy our method, notably its substantial reduction false alarms remarkable gains accuracy. More generally, exhibits versatility can be extrapolated broader spectrum tasks scenarios.