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Paper Abstract and Keywords
Presentation 2024-03-12 15:35
Data Analysis and Visualization Method for Equipment Anomaly Detection by Using Acoustic Data
Koichiro Kawano, Nobayashi Daiki, Tsukamo Kazuya, Mizumachi Mitsunori, Ikenaga Takeshi (KIT) SITE2023-82 IA2023-88
Abstract (in Japanese) (See Japanese page) 
(in English) Manual inspections of general equipment present many challenges in terms of manpower, time, and labor costs. In addition, the timing gap between inspections and breakdowns makes proper maintenance difficult, which can have a negative impact on the stable operation of equipment.
In order to solve these problems, this study develops a system to record the driving noise of hydraulic pumps in substations over a long period of time and visualize the data. At the same time, we will analyze the correlation between hydraulic pump operation time and weather data, and verify whether we can determine safe operation time ranges and predictive signs of failure from the results. This will contribute to the stable operation of equipment and provide a means to achieve appropriate maintenance without relying on manual inspections. To evaluate the effectiveness of the system, a series of operations from data collection, analysis, and visualization were verified for 11 hydraulic pumps, and the safe operating range of the equipment was estimated from the results of correlation analysis, linear regression analysis, and residual analysis.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / Acoustic Data / Anomaly Detection / Visualization / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 422, IA2023-88, pp. 86-91, March 2024.
Paper # IA2023-88 
Date of Issue 2024-03-05 (SITE, IA) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SITE2023-82 IA2023-88

Conference Information
Committee IA SITE IPSJ-IOT  
Conference Date 2024-03-12 - 2024-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyakojima City Future Creation Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet and Information Ethics Education, etc. 
Paper Information
Registration To IA 
Conference Code 2024-03-IA-SITE-IOT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data Analysis and Visualization Method for Equipment Anomaly Detection by Using Acoustic Data 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) Acoustic Data  
Keyword(3) Anomaly Detection  
Keyword(4) Visualization  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Koichiro Kawano  
1st Author's Affiliation Kyushu Institute of Technology (KIT)
2nd Author's Name Nobayashi Daiki  
2nd Author's Affiliation Kyushu Institute of Technology (KIT)
3rd Author's Name Tsukamo Kazuya  
3rd Author's Affiliation Kyushu Institute of Technology (KIT)
4th Author's Name Mizumachi Mitsunori  
4th Author's Affiliation Kyushu Institute of Technology (KIT)
5th Author's Name Ikenaga Takeshi  
5th Author's Affiliation Kyushu Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2024-03-12 15:35:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # SITE2023-82, IA2023-88 
Volume (vol) vol.123 
Number (no) no.421(SITE), no.422(IA) 
Page pp.86-91 
#Pages 6 
Date of Issue 2024-03-05 (SITE, IA) 

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