2D-CNN Based Classification on Water Leakage Identification in Automatic Pump

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dc.contributor.authorSatthamsakul, Sutham
dc.contributor.authorRattanakun, Kritsana
dc.contributor.authorKhummongkol, Rojanee
dc.contributor.authorTangsrirat, Worapong
dc.date.accessioned2026-08-06T10:53:34Z
dc.date.available2026-08-06T10:53:34Z
dc.date.issued2026-01-01
dc.description.abstractThis study proposes an approach for classification of unusual in automatic water pump systems, especially on a water leakage, one of the key issues that can significantly affect system efficiency and cause serious damage. The method utilizes motor current signals, which are transformed into 2D spectrograms using the Short-Time Fourier Transform (STFT). These spectrograms are then classified by a 2D Convolutional Neural Network (2D-CNN) Designed to determine between usual and unusual operating conditions with high accuracy. Experimental results indicate that the model can effectively detect unusual and leakage events when trained with appropriate parameters, such as a learning rate of 0.001 and 60 training epochs. This model serves as an efficient tool for preventing system failures and reducing maintenance costs in automatic water pump systems.
dc.identifier.citation2026 16th International Conference on Power Energy and Electrical Engineering Cpeee 2026, 241-246, 2026
dc.identifier.doi10.1109/CPEEE69412.2026.11521645
dc.identifier.other2-s2.0-105041504887
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17597
dc.source2026 16th International Conference on Power Energy and Electrical Engineering Cpeee 2026
dc.subject2D Convolutional Neural Network
dc.subjectDeep Learning
dc.subjectLearning rate
dc.subjectShort-Time Fourier Transform
dc.subjectWater Leakage Classification
dc.title2D-CNN Based Classification on Water Leakage Identification in Automatic Pump
dc.typeConference Paper

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