Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods

Abstract

This thesis is concerned with the development and study of a pattern recognition system for siphon and sewer condition/defect analysis based on acoustic characteristics. Pattern recognition has been studied and used widely in many fields including: identification and authentication; medical diagnosis and musical modelling. Audio based classification and research has been mainly focusing on speech recognition and music retrieval, but few applications have attempted to use acoustic characteristics for underground pipe condition classification. Traditional CCTV inspection methods are relatively expensive and subjective so remote techniques have been developed to overcome this concern and increase the inspection efficiency. The acoustic environment provides a rich source of information about the internal conditions of a pipe. This thesis reports on a classification system based on measuring the direct and reflected acoustic signals and describing the energy spectrum for each condition/pipe defect. A K-nearest neighbour classifier (KNN) and Support vector machines (SVMs) classifier have been adopted to train the classification system to identify sediment and pipe surface defects by comparing the measured acoustic signals with a database containing a range of typical conditions. Laboratory generated data and field collected data were used to train the proposed system and evaluate its ability. The overall accuracy of the system recognizing blockage and structural aspects in each of the series of experiments varies between 70% and 95%.