Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition

• Published in: Measurement : journal of the International Measurement Confederation Vol. 115; pp. 39 - 44
• Main Authors: Li, Suzhen, Song, Yanjue, Zhou, Gongqi
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.02.2018; Elsevier Science Ltd
• Subjects: Acoustic emission; Acoustics; Artificial neural network; Artificial neural networks; Deformation mechanisms; Emission analysis; Failure; Faucets; Feature extraction; Kurtosis; Leak detection; Municipalities; Neural networks; Nodular iron; Pattern recognition; Peak frequency; Pipes; Segments; Socket joint; Studies; Water distribution; Water engineering; Water leak detection; Water pipes; Acoustic emission; Pattern recognition; Artificial neural network; Socket joint; Water leak detection
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2017.10.021

Early leak detection is of great importance for life-cycle maintenance and management of municipal pipeline system. Due to economic and technical efficiency, ductile iron pipe segments and socket joints are widely used in practice to construct water distribution systems. The ductile configuration of the socket joint allowing for large deformation constitutes the most common cause for water leakage. Using acoustic emission (AE) techniques, this paper presents an experimental study on leak detection of a water distribution system subject to failure of socket joint. The acoustic characteristics of leak signals in the socket and spigot pipe segments are investigated. After feature extraction and selection, a classifier based on artificial neural network (ANN) is established. It has been validated that the dominant frequencies of the AE leak signals due to the failure of the socket joint concentrate on 0–10 kHz. The proposed ANN-based method can achieve good estimation accuracy of 97.2% and 96.9% by using the feature set {Peak, Mean, Peak Frequency, Kurtosis} and {Mean, Peak Frequency}.