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Authors: Yavor Todorov 1 ; Sebastian Feller 1 and Roger Chevalier 2

Affiliations: 1 FCE Frankfurt Consulting Engineers GmbH, Germany ; 2 EDF, France

Keyword(s): Knowledge Discovery Process, Data Mining, Pattern Recognition, Motif Discovery, Non-trivial Sequence.

Related Ontology Subjects/Areas/Topics: Business Analytics ; Change Detection ; Data Engineering ; Engineering Applications ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Intelligent Fault Detection and Identification ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; System Modeling

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 k nowledge discovery process. (More)

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Paper citation in several formats:
Todorov, Y.; Feller, S. and Chevalier, R. (2015). Making the Investigation of Huge Data Archives Possible in an Industrial Context - An Intuitive Way of Finding Non-typical Patterns in a Time Series Haystack. In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9; ISSN 2184-2809, pages 569-581. DOI: 10.5220/0005542105690581

@conference{icinco15,
author={Yavor Todorov. and Sebastian Feller. and Roger Chevalier.},
title={Making the Investigation of Huge Data Archives Possible in an Industrial Context - An Intuitive Way of Finding Non-typical Patterns in a Time Series Haystack},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={569-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005542105690581},
isbn={978-989-758-122-9},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Making the Investigation of Huge Data Archives Possible in an Industrial Context - An Intuitive Way of Finding Non-typical Patterns in a Time Series Haystack
SN - 978-989-758-122-9
IS - 2184-2809
AU - Todorov, Y.
AU - Feller, S.
AU - Chevalier, R.
PY - 2015
SP - 569
EP - 581
DO - 10.5220/0005542105690581

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