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Paper Abstract and Keywords
Presentation 2018-09-21 09:50
Anomaly Detection for Various Operations of Machine
Kazuki Kobayashi, Masatoshi Sekine, Satoshi Ikada (OKI) PRMU2018-56 IBISML2018-33
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose an anomaly level estimation method for various operation of machine. Our proposed method has two main functions: 1) making feature extraction models automatically in both time and frequency domains, 2) estimating anomaly level in each time and frequency. The outline of our proposed method is as follows. First, the spectrogram of sensor data such as vibration data and sound data are calculated. Second, the features of them are extracted from the summarized information using deep learning. By inputting the spectrogram divided for each time or each frequency, we can obtain the vibration features for each time or each frequency. Finally, the probability density model for anomaly level estimation is built. It explains the relations between time and vibration features or, frequency and vibration feature.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomaly Detection / Anomaly Level Estimation / Vibration Analysis / Feature Extraction / Machine Learning / Neural Network / Deep Learning /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 219, PRMU2018-56, pp. 133-138, Sept. 2018.
Paper # PRMU2018-56 
Date of Issue 2018-09-13 (PRMU, IBISML) 
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 PRMU2018-56 IBISML2018-33

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2018-09-20 - 2018-09-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2018-09-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomaly Detection for Various Operations of Machine 
Sub Title (in English)  
Keyword(1) Anomaly Detection  
Keyword(2) Anomaly Level Estimation  
Keyword(3) Vibration Analysis  
Keyword(4) Feature Extraction  
Keyword(5) Machine Learning  
Keyword(6) Neural Network  
Keyword(7) Deep Learning  
Keyword(8)  
1st Author's Name Kazuki Kobayashi  
1st Author's Affiliation Oki Electric Industry Co., Ltd. (OKI)
2nd Author's Name Masatoshi Sekine  
2nd Author's Affiliation Oki Electric Industry Co., Ltd. (OKI)
3rd Author's Name Satoshi Ikada  
3rd Author's Affiliation Oki Electric Industry Co., Ltd. (OKI)
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Speaker Author-1 
Date Time 2018-09-21 09:50:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2018-56, IBISML2018-33 
Volume (vol) vol.118 
Number (no) no.219(PRMU), no.220(IBISML) 
Page pp.133-138 
#Pages 6 
Date of Issue 2018-09-13 (PRMU, IBISML) 

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