2022
DOI: 10.3390/s22020696
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TR Self-Adaptive Cancellation Based Pipeline Leakage Localization Method Using Piezoceramic Transducers

Abstract: In this paper, we propose a novel time reversal-based localization method for pipeline leakage. In the proposed method, a so-called TR self-adaptive cancellation is developed to improve the leak localization resolution. First of all, the proposed approach time reverses and back-propagates the captured signals. Secondly, the time reversed signals with the various coefficients are superposed. Due to the synchronous temporal and spatial focusing characteristic of time reversal, those time reversed signals will ca… Show more

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“…Some scholars use signal processing technology to denoise the collected signals or use adaptive elimination to improve the positioning accuracy of the time reversal location algorithm. This method is suitable for linear sensor arrays and is not suitable for safety-valve-leak location [10,11]. The positioning method combining the cross-time spectrum (CTFS), hyperbolic positioning algorithm, and acoustic emission sensor array has been proposed to locate large-scale pressure and long-distance pipeline leakage, but the denoising processing of the collected signal is not considered, resulting in the positioning accuracy not reaching the range within 3% [12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…Some scholars use signal processing technology to denoise the collected signals or use adaptive elimination to improve the positioning accuracy of the time reversal location algorithm. This method is suitable for linear sensor arrays and is not suitable for safety-valve-leak location [10,11]. The positioning method combining the cross-time spectrum (CTFS), hyperbolic positioning algorithm, and acoustic emission sensor array has been proposed to locate large-scale pressure and long-distance pipeline leakage, but the denoising processing of the collected signal is not considered, resulting in the positioning accuracy not reaching the range within 3% [12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%