Content Provider	IEEE Xplore Digital Library
Author	Todorov, Y. Feller, S. Chevalier, R.
Copyright Year	2015
Description	Author affiliation: R&D Rech. et Dev., EDF, Chatou, France (Chevalier, R.) \|\| FCE Frankfurt Consulting Eng. GmbH, Hochheim, Germany (Todorov, Y.; Feller, S.)
Abstract	Modern nuclear power plants are equipped with a vast variety of sensors and measurement devices. Vibrations, temperatures, pressures, flow rates are just the tip of the iceberg representing the huge database composed of the recorded measurements. However, only storing the data is of no value to the information-centric society and the real value lies in the ability to properly utilize the gathered data. In this paper, we propose a knowledge discovery process designed to identify non-typical or anomalous patterns in time series data. The foundations of all the data mining tasks employed in this discovery process are based on the construction of a proper definition of non-typical pattern. Building on this definition, the proposed approach develops and implements techniques for identifying, labelling and comparing the sub-sections of the time series data that are of interest for the study. Extensive evaluations on artificial data show the effectiveness and intuitiveness of the proposed knowledge discovery process.
Sponsorship	INSTICC
Starting Page	569
Ending Page	581
File Size	1166109
Page Count	13
File Format	PDF
ISBN	9789897581496
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	SCITEPRESS
Subject Keyword	Temperature measurement Pattern Recognition Databases Aggregates Motif Discovery Time series analysis Non-trivial Sequence Knowledge Discovery Process Knowledge discovery Data Mining Data mining Approximation methods
Content Type	Text
Resource Type	Article

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