Third-Party Damage Monitoring Technology for Long-Distance Natural Gas Pipelines Based on EEMD

This is the authors' abstract. We don't add key points for this paper.

Summary Research into third-party damage events affecting the safe transportation of oil and gas pipelines has revealed challenges caused by the long pipeline distances and the large volume of vibration signal data collected. This makes it difficult to effectively identify disturbances. The available signal data are massive but contains few discernible events. A monitoring method based on phase-sensitive optical fiber backscatter Rayleigh scattering interferometry (Φ-OTDR) and an ensemble empirical mode decomposition (EEMD)-neural network fusion algorithm is proposed. By utilizing Φ-OTDR technology and laying single-core optical fibers alongside the pipeline in the same trench, disturbance signals along the pipeline are collected. Combined with the EEMD algorithm to handle the nonstationarity of the signals, key features are extracted from six types of events, including mechanical damage, knocking damage, vehicle passage, and manual excavation. After feature extraction, neural networks are used for classification and recognition. Experimental data show that this technology can achieve a high recognition rate of 99.0% and a low false alarm rate of 1.0%. Furthermore, the classification effect of the model is further verified through confusion matrix analysis. The practical application of this technology significantly improves the monitoring accuracy of pipeline disturbance events, effectively reduces the false alarm rate, and provides strong technical support for the safety protection of long-distance oil and gas pipelines.