Abstract
With the development of technology, the total extent of global pipeline transportation is also increased. However, the traditional long-distance optical fiber prewarning system has poor real-time performance and high false alarm rate when recognizing events threatening pipeline safety. The same vibration signal would vary greatly when collected in different soil environments and this problem would reduce the signal recognition accuracy of the prewarning system. In this paper, we studied this effect theoretically and analyzed soil vibration signals under different soil conditions. Then we studied the signal acquisition problem of long-distance gas and oil pipeline prewarning system in real soil environment. Ultimately, an improved high-intelligence method was proposed for optimization. This method is based on the real application environment, which is more suitable for the recognition of optical fiber vibration signals. Through experiments, the method yielded high recognition accuracy of above 95%. The results indicate that the method can significantly improve signal acquisition and recognition and has good adaptability and real-time performance in the real soil environment.
Highlights
Pipeline integrity is vital to the transmission of gas and oil [1,2,3,4]
An improved high-intelligence method was proposed for optimization. is method can solve the problem that the same vibration signal would vary greatly when collected in different soil environments, which would reduce the signal recognition accuracy of the prewarning system. is method is based on the real application environment, which has practical significance for industrial applications. e improved highintelligence method is a new neural network method, which has the advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)
It is a new attempt in the field of optical fiber prewarning. rough experiments, it is verified that this improved NN has good adaptability and real-time performance
Summary
Pipeline integrity is vital to the transmission of gas and oil [1,2,3,4]. Timely discovering the intrusion events around the pipelines and preventing pipeline leakage is the major direction of current researches. e optical fiber prewarning system (OFPS) is mainly used in underground cable and pipeline transportation safety. In comparison to other methods, distributed optical fiber sensing technology is used in the OFPS with higher positioning accuracy and sensitivity and lower investment cost [5,6,7,8]. The OFPS includes two components of Pattern Recognition System (PRS) and Distributed Acoustic sensing System (DAS). DAS is a distributed acoustic wave sensing system enabling remote monitoring, and PRS is utilized to recognize and classify various events from the DAS-collected data. The DAS based on the Φ-Optical Time-domain Reflectometry (V-OTDR) principle is utilized to collect the soil vibration signal [9,10,11]. Commonly used gas and oil pipeline prewarning technologies mainly include optical fiber sensing and seismic wave detection. Since the installation and maintenance of each sensor requires excavation, which leads to higher use and maintenance costs, this technology is only suitable for monitoring of key pipe
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