Investigating pump cavitation based on audio sound signature recognition using artificial neural network

How to investigate the occurrence of cavitation in the pump? Several studies have shown the sound characteristic that occurs during cavitation. This research attemps to build a pump cavitation detection system based on the audio signal of the operating pump. Audio signal is recorded using a micropho...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1569; no. 3; pp. 32044 - 32049
Main Authors Arendra, Anis, Akhmad, Sabarudin, Winarso, Kukuh, Herianto
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.07.2020
Subjects
Algorithms
Artificial neural networks
Cavitation
Classifiers
Correlation analysis
Feature extraction
Frequency domain analysis
Neural networks
Physics
Sound cards
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Summary:How to investigate the occurrence of cavitation in the pump? Several studies have shown the sound characteristic that occurs during cavitation. This research attemps to build a pump cavitation detection system based on the audio signal of the operating pump. Audio signal is recorded using a microphone through a computer sound card. Then perform the frequency domain feature extraction and the correlation analysis for feature selection. From this process, 9 frequency domain features are selected as the artificial neural network classifier input. This artificial neural network classifier is trained with the Resilient backprogation algorithm The performance of this detection system is able to determine the existence of cavitation with an accuracy rate of 82.5%.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1569/3/032044