Abstract. With the development of technology, the total extent of global pipeline transportation is also increased each year. Nevertheless, owing to the wide distribution of gas and oil pipelines and the complex laying environment, the traditional long-distance optical fiber pre-warning system (OFPS) has a high false alarm rate when recognizing events threatening the pipeline safety, and it is difficult to define the type of intrusion events. Therefore, an improved high-intelligence long-distance OFPS was proposed, and the generalized adaptability of the system in various environments was studied. Φ-optical time-domain reflectometry technology was used in the distributed sensing part of the system, and a neural network (NN) was used in the signal recognition part to identify and classify intrusion events. Three methods were used in this system including an improved NN, a wavelet packet decomposition-based artificial NN, and a five-layer deep NN. Finally, through experiments in these three methods, the adaptability of the system was explored. The results show that the system possesses an excellent classification effect in the recognition of intrusion events with an average recognition rate reaching over 95%. Thus, it has good adaptability under various real environmental circumstances.

中文翻译：

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一种改进型高智能远距离光纤预警系统的适应性研究

摘要。随着技术的发展，全球管道运输的总量也在逐年增加。然而，由于油气管道分布广泛，敷设环境复杂，传统的远距离光纤预警系统（OFPS）在识别威胁管道安全的事件时误报率高，难以实现。定义入侵事件的类型。为此，提出了一种改进的高智能长距离OFPS，研究了系统在各种环境下的泛化适应性。系统分布式传感部分采用Φ-光学时域反射技术，信号识别部分采用神经网络（NN）对入侵事件进行识别和分类。该系统使用了三种方法，包括改进的神经网络、基于小波包分解的人工神经网络和五层深度神经网络。最后，通过对这三种方法的实验，探讨了系统的适应性。结果表明，该系统对入侵事件的识别具有良好的分类效果，平均识别率达到95%以上。因此，它在各种真实环境情况下具有良好的适应性。