An improved pipeline leak detection and localization method based on compressed sensing and event-triggered particle filter. Issue 15 (October 2021)

• Record Type: Journal Article
• Authors: He, Ning; Qian, Cheng; Li, Ruoxia; Zhang, Meng
• Is Part Of: Journal of the Franklin Institute. Volume 358:Issue 15(2021)
• Page Start: 8085
• Page End: 8108
• Publication Date: 2021-10
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2021.08.012
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper proposes an improved model based pipeline leak detection and localization method based on compressed sensing (CS) and event-triggered (ET) particle filter (ET-PF). First, the state space model of the pipeline system is established based on the characteristic line method. Then, the CS method is used to preprocess the sensor signals to recover the potentially lost leak information which is caused by the low sampling frequency of the industrial pipeline sensors, and an event based beetle antennae search (BAS) particle filter (BAS-PF) is proposed to improve the accuracy and efficiency of the pipeline state estimation. Finally, a pipeline leak detection and localization method is developed based on the proposed signal processing, and state estimation algorithms, as well as a pipeline partition strategy. Experiment results show that the proposed method can accurately detect and locate the leak of the pipeline system with a localization error of about 1.4%.