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|Title:||Acoustic Signal Classification by Support Vector Machine for Pipe Crack Early Warning in Smart Water Networks|
|Citation:||Journal of Water Resources Planning and Management, 2022; 148(7):04022035-1-04022035-10|
|Publisher:||American Society of Civil Engineers (ASCE)|
|Chi Zhang, Mark L. Stephens, Martin F. Lambert, A.M.ASCE, Bradley J. Alexander, and Jinzhe Gong|
|Abstract:||Uncontrolled pipe breaks are a challenge for water utilities all over the world. This paper describes a technique that enables pipe cracks to be identified at an early stage before they become uncontrolled breaks by utilizing a permanent acoustic monitoring system as part of a smart water network. Multiple acoustic features are selected and extracted from recorded wave files that are associated with proactive repair and uncontrolled pipe break events. The extracted acoustic features and the associated wave file labels (as either crack/leak noise or no crack/ leak noise) are used to train a support vector machine model. The trained model has been operationalized in the South Australia Water Corporation’s smart water network analytics platform to process incoming new acoustic wave files in a near-real-time manner. If the acoustic wave file is classified as a pipe crack/leak, an alarm is sent to an investigation crew such that leak localization can be conducted and repairs started. The successful detection of multiple pipe cracks/leaks by the developed model after its implementation proves that it is an effective tool to enable proactive management of pipe breaks in water distribution systems. DOI: 10.1061/(ASCE)WR.1943-5452.0001570. © 2022 American Society of Civil Engineers.|
|Keywords:||Smart water network; Leak detection; Crack detection; Support vector machine; Accelerometers; Acoustic signal|
|Appears in Collections:||Civil and Environmental Engineering publications|
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