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Authors: Fabien Bouleau 1 and Christoph Schommer 2

Affiliations: 1 SES Engineering and Chateau de Betzdorf, Luxembourg ; 2 University of Luxembourg, Luxembourg

ISBN: 978-989-758-015-4

Keyword(s): Data Mining, Time Series, Machine Learning, Pattern Identification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge Representation and Reasoning ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Spacecrafts provide a large set of on-board components information such as their temperature, power and pressure. This information is constantly monitored by engineers, who capture the outliers and determine whether the situation is abnormal or not. However, due to the large quantity of information, only a small part of the data is being processed or used to perform anomaly early detection. A common accepted research concept for anomaly prediction as described in literature yields on using projections, based on probabilities, estimated on learned patterns from the past (Fujimaki et al., 2005) and data mining methods to enhance the conventional diagnosis approach (Li et al., 2010). Most of them conclude on the need to build a pattern identity chart. We propose an algorithm for efficient outlier detection that builds an identity chart of the patterns using the past data based on their curve fitting information. It detects the functional units of the patterns without apriori knowledge wi th the intent to learn its structure and to reconstruct the sequence of events described by the signal. On top of statistical elements, each pattern is allotted a characteristics chart. This pattern identity enables fast pattern matching across the data. The extracted features allow classification with regular clustering methods like support vector machines (SVM). The algorithm has been tested and evaluated using real satellite telemetry data. The outcome and performance show promising results for faster anomaly prediction. (More)

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Paper citation in several formats:
Bouleau, F. and Schommer, C. (2014). Finding Outliers in Satellite Patterns by Learning Pattern Identities.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 113-120. DOI: 10.5220/0004814301130120

author={Fabien Bouleau. and Christoph Schommer.},
title={Finding Outliers in Satellite Patterns by Learning Pattern Identities},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},


JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Finding Outliers in Satellite Patterns by Learning Pattern Identities
SN - 978-989-758-015-4
AU - Bouleau, F.
AU - Schommer, C.
PY - 2014
SP - 113
EP - 120
DO - 10.5220/0004814301130120

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