Hierarchical Modelling of Industrial System Reliability with Probabilistic Logic

Kamil Dedecius, Pavel Ettler

2014

Abstract

The use of Bayesian methods in dynamic assessment of system reliability is inevitably limited by computational difficulties arising from non-conjugate prior distributions. This contribution proposes an alternative framework, based on the combination of Bayesian methods and the subjective logic. The advantage of the former – consistent and exhaustive representation of available statistical knowledge, is extended by the latter, allowing computationally feasible combination of this knowledge at any level of the observed system using logic operations. The resulting methodology is currently under development in order to enlarge the capability of an intended novel industrial hierarchical condition monitoring system.

References

  1. Dedecius, K., Nagy, I., and KárnÉ, M. (2012). Parameter tracking with partial forgetting method. International Journal of Adaptive Control and Signal Processing, 26(1):1-12.
  2. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2003). Bayesian Data Analysis, Second Edition. Chapman & Hall/CRC.
  3. Hamada, M. S., Wilson, A. G., Reese, C. S., and Martz, H. F. (2008). Bayesian Reliability.
  4. Jøsang, A. (2001). A Logic for Uncertain Probabilities. Int. J. Unc. Fuzz. Knowl. Based Syst., 09(03):279-311.
  5. Jøsang, A. (2008). Conditional Reasoning with Subjective Logic. Journal of Multiple-Valued Logic and Soft Computing, 15(1):5-38.
  6. Jøsang, A. and McAnally, D. (2005). Multiplication and comultiplication of beliefs. International Journal of Approximate Reasoning, 38(1):19-51.
  7. Peterka, V. (1981). Bayesian approach to system identification In P. Eykhoff (Ed.) Trends and Progress in System Identification, Oxford, U.K.: Pergamon Press, 239- 304.
  8. Raftery, A., KárnÉ, M., and Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics, 52(1):52-66.
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Paper Citation


in Harvard Style

Dedecius K. and Ettler P. (2014). Hierarchical Modelling of Industrial System Reliability with Probabilistic Logic . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 133-139. DOI: 10.5220/0005007001330139


in Bibtex Style

@conference{icinco14,
author={Kamil Dedecius and Pavel Ettler},
title={Hierarchical Modelling of Industrial System Reliability with Probabilistic Logic},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={133-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005007001330139},
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 - Hierarchical Modelling of Industrial System Reliability with Probabilistic Logic
SN - 978-989-758-039-0
AU - Dedecius K.
AU - Ettler P.
PY - 2014
SP - 133
EP - 139
DO - 10.5220/0005007001330139