A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System

Thang Bui Quy, Sohaib Muhammad and Jong-Myon Kim
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Thang Bui Quy: School of Computer Engineering, University of Ulsan, Ulsan 44610, Korea
Sohaib Muhammad: School of Computer Engineering, University of Ulsan, Ulsan 44610, Korea
Jong-Myon Kim: School of Computer Engineering, University of Ulsan, Ulsan 44610, Korea

Energies, 2019, vol. 12, issue 8, 1-18

Abstract: This paper proposes a reliable leak detection method for water pipelines under different operating conditions. This approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, an intermediate quantity, which contains the symptoms of a leak and keeps its characteristic adequately stable even when the environmental conditions change, is calculated. Finally, a k-nearest neighbor (KNN) classifier is trained using features extracted from the transformed signals to identify leaks in the pipeline. Experiments are conducted under different conditions to confirm the effectiveness of the proposed method. The results of the study indicate that this method offers better quality and more reliability than using features extracted directly from the AE signals to train the KNN classifier. Moreover, the proposed method requires less training data than existing techniques. The transformation method is highly accurate and works well even when only a small amount of data is used to train the classifier, whereas the direct AE-based method returns misclassifications in some cases. In addition, robustness is also tested by adding Gaussian noise to the AE signals. The proposed method is more resistant to noise than the direct AE-based method.

Keywords: acoustic emissions; k-nearest neighbor; leak detection; pipeline diagnostics; reliability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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