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.
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