Quantification of Information Uncertainty for the Purpose of Condition Monitoring

Pavel Ettler, Kamil Dedecius

Abstract

Pervasive uncertainty of information which affects to some extent functionality of every control and information system concerns naturally the condition monitoring systems as well. Uncertainty can practically be disregarded when monitoring a single component, but it should be taken into account when compounding extensive amount of information within a hierarchical diagnostic system. When using uncertain information for expression of inner system's relations, probabilistic and namely subjective logic may do a good turn. However, the key problem remains how to quantify the uncertainty on the lowermost level of the monitoring system. The paper introduces several solutions based on inspection of either a single measured signal or a couple of correlated signals.

References

  1. Dedecius, K. and Ettler, P. (2013). Overview of bounded support distributions and methods for Bayesian treatment of industrial data. In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013), pages 380- 387, Reykjavík, Iceland.
  2. Ettler, P. and Dedecius, K. (2014). Probabilistic reasoning in service of condition monitoring. In Proceedings of the 11th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies CM2014/MFPT2014, Manchester, UK.
  3. Ettler, P. and Puchr, I. (2013). Utilization of Matlab classes to streamline experimental software. In Proceedings of the International Conference Technical Computing Prague (TCP 2013), Prague, Czech Republic.
  4. Jirsa, L. and Pavelková, L. (2014). Testing of sensor condition using gaussian mixture model. In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014), Vienna, Austria.
  5. Jøsang, A. (2013). Subjective logic (draft). University of Oslo. Available on-line at http://folk.uio.no/josang/papers/subjective logic.pdf/.
  6. Jøsang, A. and McAnally, D. (2005). Multiplication and comultiplication of beliefs. International Journal of Approximate Reasoning, 38(1):19-51.
  7. KárnÉ, M., Böhm, J., Guy, T. V., and Nedoma, P. (2003). Mixture-based adaptive probabilistic control. International Journal of Adaptive Control and Signal Processing, 17(2):119-132.
  8. Kocjan, W. (2008). Learning Nagios 3.0. Packt Publishing.
  9. Pavelková, L. and Jirsa, L. (2014). Evaluation of sensor signal health using model with uniform noise. In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014), Vienna, Austria.
  10. Peterka, V. (1981). Bayesian Approach to System Identification In P. Eykhoff (Ed.) Trends and Progress in System Identification. Pergamon Press, Eindhoven, Netherlands.
Download


Paper Citation


in Harvard Style

Ettler P. and Dedecius K. (2014). Quantification of Information Uncertainty for the Purpose of Condition Monitoring . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 127-132. DOI: 10.5220/0005006901270132


in Bibtex Style

@conference{icinco14,
author={Pavel Ettler and Kamil Dedecius},
title={Quantification of Information Uncertainty for the Purpose of Condition Monitoring},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={127-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005006901270132},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Quantification of Information Uncertainty for the Purpose of Condition Monitoring
SN - 978-989-758-039-0
AU - Ettler P.
AU - Dedecius K.
PY - 2014
SP - 127
EP - 132
DO - 10.5220/0005006901270132