In-line Acoustic Device Leakage Detection In Water Distribution Pipes Based On Wavelet And Neural Network

The conventional fixed acoustic sensors leak detection methods have been demonstrated to be very practical for locating leakages in water distribution pipelines.However,these methods demand proper installation of sensors,and therefore cannot be implemented on buried long water distribution pipelines for condition assessment,early leak detection,and the estimation of leakage size effect.Due to these limitations,the in-line acoustic device is developed which consists of acoustic sensors.The device will travel by flow of water through the pipes which record all noise events and detect the small leakages.The free-swimming device with the potential of high acoustic sensitivity is capable of detecting the small underwater leakages in the plastic water-filled pipes.Despite the fact that a number of factors influence the underwater acoustic signals,such as water flow noise.Therefore,the interpretation of the leakage and influence of leakage size is considerably challenging from the underwater measured signals.The new method is proposed for reliable leakage detection by tuning the wavelet transform to underwater acoustic signals.In this method,firstly,Short-Time Fourier Transforms(STFT)of underwater acoustic signals over a relatively long time-interval is monitored to capture the leakage-signals signature.The captured signals efficiently lead to the selection of mother wavelet(tuned wavelet)for the excellent signal localization in the time-frequency domain.Secondly,it presents algorithm structure with the modularity of wavelet and neural network,which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks.This study also validates that the time domain is not evident to the complete features regarding noisy leakage signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes.The practical application of the proposed method,the controlled experiments are designed,and acoustic signals are collected from an experimental setup by launching the free-swimming device.The measured acoustic signals are used to identify the leakage-signals signature from unwanted interfering signals(instantaneous pipe vibrations,water flow noise,pipe’s natural frequencies,and background noise).The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise,background noise,and by neural network implementation,the method improves the classification performance of extracted features.Moreover,it is also validated that the in-line acoustic device with optimal wavelet transform and neural network together can efficiently lead to reliable underwater leakage detection.