Experimental data-driven streamlined approach for tampering detection in oil and gas pipelines

Pipelines are essential links of the energy sector for transporting and distributing energy products across countries and from refineries to terminals. With the increasing value of energy products, pipelines become vulnerable targets for tampering activities. Oil and gas pipelines are exposed to various forms of tampering events. These incidents can result in significant safety risks and economic losses. Continuous monitoring and detection of these events is essential due to the critical nature of these systems. Even minor disturbances may lead to severe problems. Therefore, it is important not to overlook any minor deviation in the data. The proposed approach develops an experimental data-driven framework to capture and interpret minor deviations in vibration signals that reflect early signs of abnormality. This streamlined approach covers all aspects, from initial data collection using accelerometers positioned along the pipeline to advanced statistical analysis. Initial statistical evaluation is followed by spectrogram-based time-frequency analysis to visualize transient changes. Upon detecting significant changes, ANOVA testing and post-hoc analysis quantify group differences. The proposed method efficiently monitors and addresses changes in collected data under three distinct conditions of the pipe during experimental analysis. Findings reveal substantial differences in measurements corresponding to different pipeline states. Results highlight the methodology’s effectiveness in identifying both major faults and subtle variations arising from tampering or other conditions. This study provides valuable insights into pipeline monitoring and offers a strong foundation for future research and practical applications, demonstrating a scalable basis for integrating advanced analytics into pipeline safety systems.