Water leak detection based on convolutional neural network using actual leak sounds and the hold-out method
Youngwook Nam, Yasuhiro ARAI, Takaharu KUNIZANE, Atsushi Koizumi
Abstract
Abstract The main purpose of this study was to investigate whether machine learning can be used to detect leak sounds in the field. A method for detecting water leaks was developed using a convolutional neural network (CNN), after taking recurrence plots and visualising the time series as input data. In collaboration with a pipeline restoration company, 20 acoustic datasets of leak sounds were recorded by sensors at 10 leak sites. The detection ability of the constructed CNN model was tested using the hold-out method for the 20 cases: 19 showed more than 70% accuracy, of which 15 showed more than 80%.
Topics & Concepts
LeakConvolutional neural networkLeak detectionComputer sciencePipeline (software)Speech recognitionField (mathematics)Artificial intelligencePattern recognition (psychology)EngineeringMathematicsPure mathematicsEnvironmental engineeringProgramming languageWater Systems and OptimizationMusic and Audio ProcessingPhonocardiography and Auscultation Techniques