Abstract
Due to long-term usage, natural disasters and human factors, pipeline leaks or ruptures may occur, resulting in serious consequences. Therefore, it is of great significance to monitor and conduct real-time detection of pipeline leaks. Currently, the mainstream methods for pipeline leak monitoring mostly rely on a single signal, which have significant limitations such as single temperature being susceptible to environmental temperature interference leading to misjudgment, and single vibration signal being affected by pipeline operation noise. Based on this phenomenon, this research has built a distributed optical fiber system as an experimental platform for temperature and vibration monitoring, obtaining 3,530 sets of real-time synchronized spatial-temporal temperature and vibration signals. A dual-parameter fusion residual neural network structure has been constructed, which can extract characteristic signals from the original spatial-temporal temperature and vibration signals obtained from the above monitoring system, thereby achieving a classification accuracy of 92.16% for pipeline leak status and a leakage location accuracy of 1 m. This solves the problem of insufficient feature extraction and weak anti-interference ability in single signal monitoring. By fusing the original temperature and vibration signals, more leakage features can be extracted. Therefore, compared with single signal monitoring, this study has improved the accuracy of leakage identification and location, bridging the gap of misjudgment caused by single signal interference, and providing a basis for pipeline leakage monitoring and real-time warning in the oil industry.