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Paper Abstract and Keywords
Presentation 2019-11-28 16:25
Reliability Methodologies for Degradation Predictions Based on Hierarchical Bayesian Modeling and Machine Learning
Toru Kaise, Toyohiko Egami (Univ. of Hyogo) R2019-49
Abstract (in Japanese) (See Japanese page) 
(in English) Degradation processes are significant for making values of reliability.
Particularly, it is known that stochastic models are useful for representations of the degradation paths. In this paper, a hierarchical Bayesian VG (Variances Gamma) model is proposed for a representation of the degradation phenomenon, moreover, the real option method based on a simulation is applied to the reliability evaluation using the model with machine learning methods. In particular, the hierarchical Bayesian model is constructed in a state space model with an observation equation, and the Boltzmann machine is applied. It is also shown that the Bayesian estimation for the degradation analysis is handled based on MCMC (Markov Chain Monte Carlo) methodology and a method for comparisons of the model based on the information criterion EIC is proposed.
Keyword (in Japanese) (See Japanese page) 
(in English) Degradation Process / Hierarchical Bayes / EIC / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 315, R2019-49, pp. 35-38, Nov. 2019.
Paper # R2019-49 
Date of Issue 2019-11-21 (R) 
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)
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Conference Information
Committee R  
Conference Date 2019-11-28 - 2019-11-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Central Electric Club 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reliability of semiconductor and electronic devices, Reliability general 
Paper Information
Registration To R 
Conference Code 2019-11-R 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reliability Methodologies for Degradation Predictions Based on Hierarchical Bayesian Modeling and Machine Learning 
Sub Title (in English)  
Keyword(1) Degradation Process  
Keyword(2) Hierarchical Bayes  
Keyword(3) EIC  
1st Author's Name Toru Kaise  
1st Author's Affiliation University of Hyogo (Univ. of Hyogo)
2nd Author's Name Toyohiko Egami  
2nd Author's Affiliation University of Hyogo (Univ. of Hyogo)
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Date Time 2019-11-28 16:25:00 
Presentation Time 25 
Registration for R 
Paper # IEICE-R2019-49 
Volume (vol) IEICE-119 
Number (no) no.315 
Page pp.35-38 
#Pages IEICE-4 
Date of Issue IEICE-R-2019-11-21 

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