Open AccessInvestigating pump cavitation based on audio sound signature recognition using artificial neural network
Author(s) -
Anis Arendra,
Akhmad Sabarudin,
Kukuh Winarso,
- Herianto
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1569/3/032044
Subject(s) - artificial neural network , computer science , audio signal , microphone , pattern recognition (psychology) , speech recognition , artificial intelligence , cavitation , classifier (uml) , feature extraction , frequency domain , time domain , acoustics , computer vision , sound pressure , speech coding , telecommunications , physics
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%.