Detecting gas pipeline leaks in sandy soil with fiber-optic distributed acoustic sensing

Chen, Zhuo; Zhang, Cheng-Cheng; Shi, Bin; Zhang, Yan; Wang, Zheng; Wang, Hao; Xie, Tao

doi:10.1016/j.tust.2023.105367

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…In addition to its obvious contribution to safety and incident prevention, the other reason behind NDT's relevance is that it allows inspection and evaluation without affecting specimen serviceability, as its name suggests. Regarding NDT in pipeline systems, multiple techniques have been proposed such as ultrasonic testing (UT) [4][5][6], radiography testing (RT) [7,8], ground-penetrating radar (GPR) [9], infrared thermography testing (IFT) [10], distributed acoustic sensing [11], active distributed temperature sensing [12], acoustic emission (AE) [13][14][15][16][17][18], etc. Each of these techniques presents various advantages and disadvantages which can best serve different applications.…”

Section: Introductionmentioning

confidence: 99%

Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest

Nguyen,

Ahmad,

Kim

2023

Sensors

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In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy.

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