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.
Highlights
Water pipeline systems are required to accomplish commercial and domestic activities
This paper proposes an algorithm to detect a small leak in a pipeline based on g(r) function because it can indicate the presence of leak as described in the previous section
State scattering based on features extracted from the acoustic emission (AE) signal of channel 3 (CH3)
Summary
Water pipeline systems are required to accomplish commercial and domestic activities. Because of the complexity of AE activity, it is difficult to establish a leak detection model directly from mathematical equations; different methods have been proposed to train the model using recorded datasets [6,7] These techniques offer high accuracy, they could be inefficient in diverse circumstances because the classifiers are trained using features extracted directly from AE signals. It delivers useful insights and results for water leak detection, but the study focused on pipelines buried under soil less influenced by external factors while AE signals from pipelines above soil in factories are prone to noise This issue can be addressed by acquiring many training datasets under different working conditions to provide enough information for the classifier. Gaussian noise is added to the AE measurements to evaluate the robustness of the classifiers trained by the two approaches
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