NDLI: Health diagnostics with unexampled faulty states using a two-fold classification method

Content Provider	IEEE Xplore Digital Library
Author	Tamilselvan, P. Pingfeng Wang Jayaraman, R.
Copyright Year	2012
Description	Author affiliation: Dept. of Industrial and Manufacturing Engineering, Wichita State University, Wichita, USA (Tamilselvan, P.; Pingfeng Wang; Jayaraman, R.)
Abstract	System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.
Starting Page	1
Ending Page	11
File Size	1189571
Page Count	11
File Format	PDF
ISBN	9781467303569
e-ISBN	9781467303583
DOI	10.1109/ICPHM.2012.6299540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Condition monitoring Real time systems Learning systems mahalanobis distance hybrid inference approach Training data Data models artificial intelligence
Content Type	Text
Resource Type	Article

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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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