Leak Detection and Location Based on ISLMD and CNN in a Pipeline

TLDR

The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. The study proposes an improved spline‑local mean decomposition (ISLMD) to remove noise and a convolutional neural network approach to detect leaks in water pipelines. The method uses ISLMD to decompose signals, extracts leak‑bearing components via cross‑correlation, converts them into images fed to AlexNet for adaptive feature extraction, and applies generalized cross‑correlation to compute time‑delay between pressure transmitters for leak localization. Experiments demonstrate that ISLMD outperforms improved local mean decomposition in locating leaks, the AlexNet model accurately detects varying leak apertures, and overall the proposed approach effectively detects and locates leaks.

Abstract

The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized cross-correlation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location.