NDLI: Automated Fault Tree Generation: Bridging Reliability with Text Mining

Content Provider	IEEE Xplore Digital Library
Author	Mukherjee, S. Chakraborty, A.
Copyright Year	2007
Description	Author affiliation: Dept of Integrated Data Syst., Siemens Corp. Res. Inc., Princeton, NJ (Mukherjee, S.; Chakraborty, A.)
Abstract	Proper preventive maintenance of complex systems, such as those used for power generation and medical diagnosis is dependent on the availability of their up-to-date reliability models. These models are constructed from historical maintenance and fault information of the equipment. Due to the complex nature of these machines, constructing these models involves significant manual effort which limits the widespread use of reliability-centric maintenance schemes. In this paper, we describe a process for automating the construction of fault trees, a class of non-state space reliability models, by analyzing maintenance data available as free-form text. It uses a combination of linguistic analysis and domain knowledge to identify the nature of the failure from short plain text descriptions of equipment faults. This information is used to automatically enrich and evolve existing fault trees for better reliability estimation
Starting Page	83
Ending Page	88
File Size	334110
Page Count	6
File Format	PDF
ISBN	0780397665
ISSN	0149144X
DOI	10.1109/RAMS.2007.328096
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-01-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault trees Text mining Power system reliability Power system modeling Preventive maintenance Power generation Medical diagnosis Availability Data analysis Failure analysis
Content Type	Text
Resource Type	Article

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