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Paper Abstract and Keywords
Presentation 2011-01-21 09:25
Fault Detection and Prediction of Industrial Plants Based on Gaussian Processes
Shinsaku Ozaki, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi) PRMU2010-175 MVE2010-100
Abstract (in Japanese) (See Japanese page) 
(in English) This report proposes a fault and pre-fault detection method for industrial plants based on Gaussian process. Industrial plants can be monitored via attached sensors that measures temperature, pressure, voltage, electric current, and so on. Based on these sensor outputs, health monitoring of the target plant can be designed. The difficulty of this design problem is that the system fault can appear as statistical abnormality observed as irregular ensemble of the sensor outputs and/or temporal abnormality observed as irregularity of the time sequences. Furthermore, those systems are operated by human and it is difficult to distinguish the abnormalities caused by human-operation and system fault. We already proposed an abnormality detection system based on ICA and linear prediction, which avoids incorrect detections of abnormalities caused by human operations by recognizing and masking them. This method, however, cannot detect the system fault during the human operation. For solving this problem, we propose a unified method for statistical and temporal abnormality detection based on Gaussian Processes in this report. This method can detect system fault under normal human operation. We confirmed the effectiveness of our method through experiments on long-term sensory data sampled from real industrial plant.
Keyword (in Japanese) (See Japanese page) 
(in English) Abnormality detection / Gaussian processes / Independent component analysis / Statistical and Temporal analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 381, PRMU2010-175, pp. 211-216, Jan. 2011.
Paper # PRMU2010-175 
Date of Issue 2011-01-13 (PRMU, MVE) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 PRMU2010-175 MVE2010-100

Conference Information
Conference Date 2011-01-20 - 2011-01-21 
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 2011-01-PRMU-MVE-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fault Detection and Prediction of Industrial Plants Based on Gaussian Processes 
Sub Title (in English)  
Keyword(1) Abnormality detection  
Keyword(2) Gaussian processes  
Keyword(3) Independent component analysis  
Keyword(4) Statistical and Temporal analysis  
1st Author's Name Shinsaku Ozaki  
1st Author's Affiliation Wakayama University (Wakayama Univ.)
2nd Author's Name Toshikazu Wada  
2nd Author's Affiliation Wakayama University (Wakayama Univ.)
3rd Author's Name Shunji Maeda  
3rd Author's Affiliation Hitachi, LTD., Production Engineering Research Laboratory (Hitachi)
4th Author's Name Hisae Shibuya  
4th Author's Affiliation Hitachi, LTD., Production Engineering Research Laboratory (Hitachi)
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Date Time 2011-01-21 09:25:00 
Presentation Time 25 
Registration for PRMU 
Paper # IEICE-PRMU2010-175,IEICE-MVE2010-100 
Volume (vol) IEICE-110 
Number (no) no.381(PRMU), no.382(MVE) 
Page pp.211-216 
#Pages IEICE-6 
Date of Issue IEICE-PRMU-2011-01-13,IEICE-MVE-2011-01-13 

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