Abstract
This article reports on an application of the k-nearest neighbours pattern recognition and classification technique to condition monitoring in a full-scale, water-filled siphon that is located beneath the underground. An experimental facility has been designed and constructed at the University of Bradford to study using acoustic waves as excitation to observe the characteristics of pipe sediments and wall damages on an underground sewer siphon. The effects of different amounts of sediment inside the siphon and different size of artificial damage on the pipe wall have been studied. The sound pressure level and acoustic energy were calculated from the acoustic signals which were recorded from three hydrophones under several representative siphon conditions to extract useful features in order that the proposed k-nearest neighbours classification algorithm could be applied. It has been proven that acoustic-based approach is capable of providing sufficient information on the condition of pipes for reliable classification and fault detection.
Highlights
Water distribution systems are key elements of urban water infrastructure
There are a large number of signal processing methods available to extract meaningful features from the recorded sounds, such as ShortTime Fourier Transform (STFT), Wavelet Transform (WT), Mel-Frequency Cepstral Coefficients (MFCCs) and Wigner-Ville Distribution (WVD).[12]
The acoustic energy calculated for a range of known conditions was used to construct a four-dimensional (4D) training data table or 4D tensor E, each element in this tensor is the value of the acoustic energy determined by the siphon condition (i), time window (j), frequency band (k) and hydrophone channel (l)
Summary
Water distribution systems are key elements of urban water infrastructure. These systems are gradually deteriorating due to their ageing, operational stresses and external environmental conditions.[1]. There are a large number of signal processing methods available to extract meaningful features from the recorded sounds, such as ShortTime Fourier Transform (STFT), Wavelet Transform (WT), Mel-Frequency Cepstral Coefficients (MFCCs) and Wigner-Ville Distribution (WVD).[12] Many of these methods have been developed and tested extensively condition classification with acoustic means through hidden Markov models (HMMs), k-nearest neighbours (KNN), decision trees or neural networks.[13] these algorithms have been developed and applied successfully in areas like biometrics, no extensive applications were found in the condition monitoring of buried civil facilities.[14] it is important to study and reveal the patterns that represent particular condition states, and to discover the relation between acoustic features and a series of patterns labelled by a full-scale model of a hydraulic siphon. The proposed KNN classification system is different from the other methodologies by acoustic energy and combines with KNN classifier, it is tested to be reliable under various pipe conditions including sediment and damage with pipe being surrounded by different medium
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