NDLI: An off-online fuzzy modelling method for fault prognosis with an application

Content Provider	IEEE Xplore Digital Library
Author	Zhaoqiang Wang Changhua Hu Wenbin Wang Xiaosheng Si Zhijie Zhou
Copyright Year	2012
Description	Author affiliation: Dongling School of Economics and Management, University of Science and Technology Beijing, China (Wenbin Wang) \|\| Department of Automation, Xi'an Institute of High-Tech, Shaanxi 710025, China (Zhaoqiang Wang; Changhua Hu; Xiaosheng Si; Zhijie Zhou)
Abstract	Fault prognosis plays a key role in prognostics and health management (PHM). Currently, there are many methods to predict the occurrence of a fault, but the fuzzy model has become an effective alternative owing to its advantage of using not only quantitative data but also qualitative information with fuzzy uncertainty. It is particularly useful for a dynamic system with complexity, morbidity and nonlinearity. Compared with the prediction from an offline fuzzy model, an online prediction is more desired, since we can monitor the health condition of a system and achieve fault prognosis in real time. In this paper, we develop a prediction model by firstly establishing an initial fuzzy model with offline information, then, using the online information to adjust the initial offline model in real time. The offline fuzzy model is modelled through a relevance vector machine (RVM) method and the structure of the initial fuzzy model is adjusted based on the statistical utility of a fuzzy rule using online information. The model parameters are optimized by the gradient decent (GD) algorithm. To do so,a fault prognosis algorithm is proposed on the basis of our off-online fuzzy modelling method. Finally, the proposed fuzzy modelling method and its fault prognosis algorithm are applied to a practical example. The empirical results show that our developed method has a significant improvement over the existing fuzzy modelling methods in terms of accuracy and the corresponding fault prognosis algorithm is effective.
Starting Page	1
Ending Page	7
File Size	1140275
Page Count	7
File Format	PDF
ISBN	9781457719097
ISSN	21665656
e-ISBN	9781457719110
e-ISBN	9781457719103
DOI	10.1109/PHM.2012.6228783
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real time systems Training Zirconium health condition monitoring Fuzzy model Predictive models fault prognosis off-online modelling method
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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