A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test.

Rai, Akhand; Ahmad, Zahoor; Hasan, Md. Junayed; Kim, Jong-Myon

Abstract

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.

Citation

RAI, A., AHMAD, Z., HASAN, M.J. and KIM, J.-M. 2021. A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. Sensors [online], 21(24): intelligent systems for fault diagnosis and prognosis, article 8247. Available from: https://doi.org/10.3390/s21248247

• Journal Article Type: Article
• Acceptance Date: Dec 8, 2021
• Online Publication Date: Dec 10, 2021
• Publication Date: Dec 31, 2021
• Deposit Date: May 26, 2022
• Publicly Available Date: May 26, 2022
• Journal: Sensors
• Print ISSN: 1424-8220
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 24
• Article Number: 8247
• DOI: https://doi.org/10.3390/s21248247
• Keywords: Pipeline; Leak detection; Acoustic emission; Kolmogorov–Smirnov test
• Public URL: https://rgu-repository.worktribe.com/output/1664775