OPTIMIZED STRATEGIES FOR ARCHIVING MULTI-DIMENSIONAL PROCESS DATA - Building a Fault-diagnosis Database

Sebastian Feller, Yavor Todorov, Dirk Pauli, Folker Beck

2011

Abstract

In many real-world applications such as condition monitoring of technical facilities or vehicles the amount of data to process and analyze has steadily increased during the last decades. In this paper a novel approach to data compression is presented, namely the multivariate representative of the Perceptually Important Points algorithm. Furthermore, approaches are given on how multivariate data should be dealt with to preserve all relevant multivariate information during a lossy data compression. This involves an extensive analysis of the stochastic dependencies of the process data. On the one hand the presented algorithm is able to compress the multivariate time series and on the other hand the algorithm can be easily extended to reflect a model of the original time series. It is shown that suggested multivariate compression algorithm outperforms its univariate equivalent.

Paper Citation

in Harvard Style

Feller S., Todorov Y., Pauli D. and Beck F. (2011). OPTIMIZED STRATEGIES FOR ARCHIVING MULTI-DIMENSIONAL PROCESS DATA - Building a Fault-diagnosis Database . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-74-4, pages 388-393. DOI: 10.5220/0003571803880393

in Bibtex Style

@conference{icinco11,
author={Sebastian Feller and Yavor Todorov and Dirk Pauli and Folker Beck},
title={OPTIMIZED STRATEGIES FOR ARCHIVING MULTI-DIMENSIONAL PROCESS DATA - Building a Fault-diagnosis Database},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2011},
pages={388-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003571803880393},
isbn={978-989-8425-74-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - OPTIMIZED STRATEGIES FOR ARCHIVING MULTI-DIMENSIONAL PROCESS DATA - Building a Fault-diagnosis Database
SN - 978-989-8425-74-4
AU - Feller S.
AU - Todorov Y.
AU - Pauli D.
AU - Beck F.
PY - 2011
SP - 388
EP - 393
DO - 10.5220/0003571803880393