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.
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