Study on Oil Pipeline Leakage Detection Based on Stress Wave Detection Technique and Wavelet Analysis

Jie Zhang; Xiao Dan Guan; Yan Sun; Xue Jie Wei

doi:10.4028/www.scientific.net/AMR.694-697.1368

Paper Titles

Particle Swarm Neural Network in Application of Soft Fault Diagnosis of Analog Circuit
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Study on the Application of FPGA in Magnetic Flux Leakage Acquisition System
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Design of Multi-Channel Lithium-Ion Battery Test System
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Modeling Dynamic Laser Speckle Patterns of Plant Leaves
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Study on Oil Pipeline Leakage Detection Based on Stress Wave Detection Technique and Wavelet Analysis
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An Analytical Method of Time Character of Electron Beam Current Density on Transmission Electron Microscope Imaging
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Research of Rolling Bearing Fault Feature Extraction Based on EMD and Choi-Williams
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Design and Research on the Cigarette Paper Smoldering Rate of PRS-2 Tester
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Engine Knock Detection Based on Time-Frequency Images and Support Vector Machine
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697Study on Oil Pipeline Leakage Detection Based on...

Study on Oil Pipeline Leakage Detection Based on Stress Wave Detection Technique and Wavelet Analysis

Abstract:

Based on stress wave detection technique and wavelet analysis, oil pipeline leakage detection system is designed. Through vibration sensor put on the pipeline, vibration signal of pipeline is collected. The signal is used to preliminary assessment with DSP processing unit within the system, and then the feature of threat events is extracted. The information is uploaded to the centre of security central station of system by GPRS signal. At this, signal wavelet analysis is done for leakage detection. Based on LabVIEW platform, on oil pipeline leakage monitoring interface, threat events is alarmed and shown in electronic map and the system is convenient for staff to handle with. With the test, recognized police rate of system is superior to 85%, rate of false alarm is 15% below. All knocking signal of pipeline is given right alarm information. The noise by people walk, car, wind and rain is effectively filtered.