Improved Variational Mode Decomposition in Pipeline Leakage Detection at the Oil Gas Chemical Terminals Based on Distributed Optical Fiber Acoustic Sensing System

Leakage in oil and gas transportation pipelines is a critical issue that often leads to severe hazardous accidents at oil and gas chemical terminals, resulting in devastating consequences such as ocean environmental pollution, significant property damage, and personal injuries. To mitigate these risks, timely detection and precise localization of pipeline leaks are of paramount importance. This paper employs a distributed fiber optic sensing system to collect pipeline leakage signals and processes these signals using the traditional variational mode decomposition (VMD) algorithm. While traditional VMD methods require manual parameter setting, which can lead to suboptimal decomposition results if parameters are incorrectly chosen, our proposed method introduces an improved particle swarm optimization algorithm to automatically determine the optimal parameters. Furthermore, we integrate VMD with fuzzy dispersion entropy to effectively select and reconstruct intrinsic mode functions, significantly enhancing the denoising performance. Our results demonstrate that this approach can achieve a signal-to-noise ratio of up to 24.15 dB and reduce the mean square error to as low as 0.0027, showcasing its superior capability in noise reduction. Additionally, this paper proposes a novel threshold setting technique that addresses the limitations of traditional methods, which often rely on instantaneous values and are prone to false alarms. This innovative approach significantly reduces the false alarm rate in gas pipeline leakage detection, ensuring higher detection accuracy and reliability. The proposed method not only advances the technical capabilities of pipeline leakage monitoring but also offers strong practical applicability, making it a valuable tool for enhancing the safety and efficiency of oil and gas transportation systems.