Pipeline Leakage Detection and Monitoring Model using Enhanced Multiple Signal Classification Algorithm and Hybrid Acoustic Emission Technique

Abstract

The consequences of pipeline leakages pose great multifaceted hazards, including carcinogenicity and cytotoxicity in humans exposed to leaked toxic substance from pipelines. Pipeline leak also causes environmental contamination of soil resulting to environmental pollution, fire disaster and even loss of life. Therefore, pipeline leakage detection monitoring is a crucial concern in pipeline industry for ensuring the safe and efficient operations. Background noise and detection of single leak are significant limitations of the existing pipeline monitoring and leakage detection techniques. These undesired noises can arise from multiple sources, including environmental, proximity industries, pipe vibration, and electronic interferences. This study therefore optimizes the conventional Multiple Signal Classification (MUSIC) algorithm and Acoustic Emission (AE) technique with the aim to develop a novel technique to address the effect of the background noise. The proposed method combines the advantages of the MUSIC algorithm and AE techniques with real-time monitoring to promptly and accurately detect leakages in pipeline systems. The model achieved Accuracy of 95.5%, Sensitivity of 75%, Mean Detection Time of 1.02 seconds and Response Time of 1.06 seconds. These quantitative results demonstrate the effectiveness of our proposed Enhanced MUSIC algorithm and Hybrid AE technique (Enhanced-MUSICHAE) to detect and monitor pipeline leakage. This has the potential to improve pipeline safety, reduce economic losses, and minimize environmental damages.