Adaptive Phase Transform Method for Pipeline Leakage Detection

In leak noise correlation surveys, time delay estimation (TDE) is of great importance in pinpointing a suspected leak. Conventional TDE methods involve pre-filtering processes prior to performing cross-correlation, based on a priori knowledge of the leak and background noise spectra, to achieve the...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 19; H. 2; S. 310
Hauptverfasser: Ma, Yifan, Gao, Yan, Cui, Xiwang, Brennan, Michael J., Almeida, Fabricio C.L., Yang, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI 14.01.2019
MDPI AG
Schlagworte:
LMS adaptive algorithm
phase spectrum
pipeline leakage detection
time delay estimation
time delay estimation
LMS adaptive algorithm
phase spectrum
pipeline leakage detection
ISSN:1424-8220, 1424-8220
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Zusammenfassung:In leak noise correlation surveys, time delay estimation (TDE) is of great importance in pinpointing a suspected leak. Conventional TDE methods involve pre-filtering processes prior to performing cross-correlation, based on a priori knowledge of the leak and background noise spectra, to achieve the desired performance. Despite advances in recent decades, they have not proven to be capable of tracking changes in sensor signals as yet. This paper presents an adaptive phase transform method based on least mean square (LMS) algorithms for the determination of the leak location to overcome this limitation. Simulation results on plastic water pipes show that, compared to the conventional LMS method, the proposed adaptive method is more robust to a low signal-to-noise ratio. To further verify the effectiveness of the proposed adaptive method, an analysis is carried out on field tests of real networks. Moreover, it has been shown that by using the actual measured data, improved performance of the proposed method for pipeline leakage detection is achieved. Hence, this paper presents a promising method, which has the advantages of simple implementation and ability to track changes in practice, as an alternative technique to the existing correlation-based leak detection methods.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19020310