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.
Highlights
Leaks in pipelines create fluid supply system malfunctions potentially leading to discharge of hazardous materials into the environment, undue maintenance expenses, increased repair costs, system downtime losses, and severe accidents
Leaks in a pipelines create stress waves that are transmitted through the pipe walls and recorded through acoustic emission (AE) sensors installed on the pipeline
The proposed technique first extracts the Acoustic Emission Event (AEE) features from the AE signal
Summary
Leaks in pipelines create fluid supply system malfunctions potentially leading to discharge of hazardous materials into the environment, undue maintenance expenses, increased repair costs, system downtime losses, and severe accidents. Wang et al [14] extracted frequency–width features from the time-domain pipeline signals and used them to train a support vector data description (SVDD) model to detect leaks. Xu et al [19] used the wavelet packet transform (WT) and time-domain features, such as mean value, peak value, RMS value, standard deviation, peak index, pulse index, waveform index, and root amplitude, in combination with Fuzzy SVM for identifying leaks. The time–frequency decomposition of large AE signals is time-consuming To overcome these shortcomings, this paper proposes a novel approach using AE-event features and a twosample Kolmogorov–Smirnov (KS) test for pipeline leak detection. The two-sample KS test was used to model the information in the AEE feature data and recognize pipeline leaks.
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