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

Sebastian Feller, Yavor Todorov, Dirk Pauli, Folker Beck

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.

References

  1. Bristol, E. (1990). Swinging door trending: Adaptive trend recording. In ISA National Conference Proceedings, volume 45, pages 749-753.
  2. Chen, H., Li, J., and Mohapatra, P. (2004). RACE: Time series compression with rate adaptivity and error bound for sensor networks. In Mobile Ad-hoc and Sensor Systems, 2004 IEEE International Conference on, pages 124-133. IEEE.
  3. Chevalier, R., Provost, D., and Seraoui, R. (2009). Assessment of Statistical and Classification Models For Monitoring EDFs Assets. In Sixth American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation.
  4. Chung, F., Fu, T., Luk, R., and Ng, V. (2001). Flexible time series pattern matching based on perceptually important points. In International Joint Conference on Artificial Intelligence Workshop on Learning from Temporal and Spatial Data, pages 1-7.
  5. Eruhimov, V., Martyanov, V., Raulefs, P., and Tuv, E. (2008). Supervised compression of multivariate time series data. Relation, 10(1.125):5395.
  6. Feller, S. (2009). Parameteridentifikation bei einem geregelten multidimensionalen stochastischen prozess am beispiel einer reaktorkhlpumpe. Diplomarbeit.
  7. Feller, S. and Chevalier, R. (2010). Parameter Disaggregation for High Dimensional Time Series Data on the Example of a Gas Turbine. In 38th ESReDA Seminar, Pcs, Hungary, May 4-5, 2010.
  8. Feller, S., Chevalier, R., Paul, N., and Pauli, D. (2010). Classification Methods for Failure Mode Diagnosis on the Example of Synthetic Data and RCP Leak Flow Data. In EPRI Technical Report.
  9. Fu, T. (2010). A review on time series data mining. Engineering Applications of Artificial Intelligence, pages 164-181.
  10. Fu, T., Chung, F., Ng, V., and Luk, R. (2001). Pattern discovery from stock time series using self-organizing maps. In The 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Workshop on Temporal Data Mining, pages 26-29. Citeseer.
  11. Gillespie, D. T. (1996). Exact numerical simulation of the ornstein-uhlenbeck process and its integral. Phys. Rev. E, 54(2):2084-2091.
  12. Hawkins III, S., Darlington, E., Cheng, A., and Hayes, J. (2003). A new compression algorithm for spectral and time-series data. Acta Astronautica, 52(2-6):487-492.
  13. Hines, J. W. and Garvey, D. R. (2006). Development and Application of Fault Detectability Performance Metrics for Instrument Calibration Verification and Anomaly Detection. Journal of Pattern Recognition Research, 1(1).
  14. Jones, M. C., Marron, J. S., and Sheather, S. J. (1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 91:401-407.
  15. Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. (2007). Numerical Recipes: The Art of Scientific Computing. Cambridge University Press.
  16. Stoffer, D. (1999). Detecting Common Signals in Multiple Time Series Using the Spectral Envelope. Journal of the American Statistical Association, 94(448).
  17. Thornhill, N., Shoukat Choudhury, M., and Shah, S. (2004). The impact of compression on data-driven process analyses. Journal of Process Control, 14(4):389-398.
  18. Uhlenbeck, G. E. and Ornstein, L. S. (1930). On the theory of the brownian motion. Phys. Rev., 36(5):823-841.
Download


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