Abstract
This paper proposes a general integration method which can effectively describe the characteristics of pipeline leakage and help distinguish multiple pipeline microstates. Since the rapid development of Φ-OTDR in recent years, this technology has been applied to more and more fields, such as fiber optic safety monitoring, seismic monitoring, and structural health monitoring. Among them, Φ-OTDR has the characteristic of continuous full-scale monitoring in pipeline monitoring, but there are few researches on pipeline state characteristics at present. In this paper, based on the analysis of the pipeline state with Φ-OTDR technology, a method of extracting multiple microstates of pipelines is proposed. This method combined with the peak-to-average power ratio, short-term interval zero crossing, and fractal characteristics in the frequency domain can effectively characterize the microstate of pipes and provide support for identification of more microstates of pipelines. These features reflect the common characteristics of leaks in gas pipelines and liquid pipelines. Meanwhile, their combination features can represent the small differences in pipeline states. The experimental results show that the method can effectively characterize the microstate information of the pipeline, and the recognition rate of the hybrid feature under two kinds of pipeline leakage and multipressure conditions reaches 91% and 83%.
Highlights
Pipeline is an important infrastructure in modern society
Based on the advantages of high sensitivity and fast response, Φ-OTDR can overcome the interference of humidity, low temperature, and electromagnetic radiation and will be the trend of pipeline monitoring [5,6]
Because of its high accuracy and high spatial resolution, Φ-OTDR is widely used in fields of fiber optic safety monitoring, perimeter security, submarine cable safety, and structural health monitoring [7]
Summary
Pipeline is an important infrastructure in modern society. Because of environmental corrosion, aging, and man-made damage, pipelines are prone to leak. E hybrid features consist of the peak-to-average power ratio, short-term interval zero crossing, and fractal characteristics in the frequency domain, which reflect leakage states of pipelines and represent more detailed information for multiple microstates of pipelines. These features cannot directly obtain the hiding pipeline state information, machine learning methods, such as support vector machine (SVM), C5.0, and randomforest algorithm, can be used to recognize the microstates of pipelines [16, 17].
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