Real-Time Classification of Distributed Fiber Optic Monitoring Signals Using a 1D-CNN-SVM Framework for Pipeline Safety

• Published in: Processes Vol. 13; no. 6; p. 1825
• Main Authors: Sima, Rui, Zhu, Baikang, Wang, Fubin, Wang, Yi, Zhang, Zhiyuan, Li, Cuicui, Wu, Ziwen, Hong, Bingyuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.06.2025
• Subjects: Anomalies; Artificial neural networks; Automation; Building; Classification; Fiber optics; Gas pipelines; Human motion; Latency; Machine learning; Monitoring; Natural gas; Neural networks; Pipe lines; Pipeline safety; Real time; Sensors; Signal processing; Support vector machines; Theft; China
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr13061825

The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic monitoring signals, leveraging a hybrid framework that combines the feature learning capacity of a one-dimensional convolutional neural network (1D-CNN) with the classification robustness of a support vector machine (SVM). The proposed method effectively distinguishes various pipeline-related events—such as minor leakage, theft attempts, and human movement—by automatically extracting their vibration patterns. Notably, it addresses the common shortcomings of softmax-based classifiers in small-sample scenarios. When tested on a real-world dataset collected via the DAS3000 system from Hangzhou Optosensing Co., Ltd., the model achieved a high classification accuracy of 99.92% across six event types, with an average inference latency of just 0.819 milliseconds per signal. These results demonstrate its strong potential for rapid anomaly detection in pipeline systems. Beyond technical performance, the method offers three practical benefits: it integrates well with current monitoring infrastructures, significantly reduces manual inspection workloads, and provides early warnings for potential pipeline threats. Overall, this work lays the groundwork for a scalable, machine learning-enhanced solution aimed at ensuring the operational safety of critical energy assets.