An acoustic method for condition classification of a water-filled underground siphon

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.