Leak detection in water supply pipeline with small-size leakage using deep learning networks

Pipeline transportation technology has witnessed remarkable development in recent years, while the issue of leakage poses a serious limitation to this technology's progress, especially for the small-scale leakage in water pipelines that exhibits weaker leakage signals. To address those challenges, this study investigates leak detection in water supply pipeline with an inner pipe diameter of 100 mm in the operating range of volume flow Q = 40-80 m3/h, leakage flow Ql = 0.65-1.33 m3/h and pressure p = 130-220 kPa. The characteristics and propagation of leakage pressure signals are analyzed. An adaptive improved variational mode decomposition (VMD) based on the white shark optimizer (WSO) algorithm is proposed, utilizing sample entropy as the fitness function. Additionally, tent chaotic mapping is employed for parameter initialization, mean normalization is used to minimize the influence of the DC component, and signal relative energy serves as a boundary condition. The processed data is utilized to establish leakage detection models using long short-term memory network (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and SqueezeNet framework. The performance of leakage detection models is evaluated and compared using 962 sets' data using evaluation indicators and confusion matrix. The results indicate that SqueezeNet-GRU demonstrates superior performance in pipeline leak detection and localization applications, achieving 97.40 % accuracy for the total dataset.