CONSISTENT DATA AND DECISION FUSION OF HETEROGENEOUS INFORMATION DENOISING IN COMPLEX SYSTEMS DIAGNOSIS

Mincho Hadjiski, Lyubka Doukovska

Abstract

In the diagnosis of complex industrial systems arise a lot of sever problems to solve due to the heterogeneous information sources, a large number of directly unmeasurable variables, which should be replaced by softsensing, big uncertainty of current information, temporal uncoherency of some measurements because of the very different requirements for the spectral window of corresponding signals in the different stages of the FDD (Fault Detection and Diagnosis) procedure. In the paper a hybrid approach of multistep procedure is considered for denoising of diagnostic information in order to achieve more realistic and more effective decision in a comparison with the conventional statistical approaches using some techniques from the Computational Intelligence like Neural Networks and Case- Based Reasoning. The main statements accepted in this investigation are: the different stages of complex diagnosis could require different information, different methods of partial diagnosis and different methods of decision making; the main method of hybridization is accepted to be consistent data and decision fusion; signal processing in particular diagnosis stages should be relevant to the main diagnostic goals in the stage. In the paper the proposed method for consistent fusion of data and decisions is implemented for online vibrodiagnosis of mechanical condition of the industrial mill fan of steam boiler in Power plant.

References

  1. Advances in Intelligent Decision (G.Phillips-Wren, Ed.), Springer, 2010.
  2. Belsak, A., and J. Prezelj, Analysis of Vibrations and Noise to Determine the Condition of Gear Unit, Advances in Vibration Analysis Research, No. 2, 2007.
  3. Das,S., High Level Data Fusion, Artech House, 2008.
  4. Donoho,D., Denoising by Soft-Thresholding, IEEE Trans. On Information Theory, V.41, 1995.
  5. Dong,J., D.Zhang, Y.Huang, and J.Fu, Advances in Multi-Sensor Data Fusion: Algorithms and Applications, Sensors, 9, 2009.
  6. Elmenreich,W., A Review on System Architectures for Sensor Fusion Application, LNCS 476, Springer, 2007.
  7. Hadjiski M., L. Doukovska, CBR approach for Technical Diagnostic on Mill Fan System, Comptes rendus de l'Academie bulgare des Sciences, ISSN 0861-1459, 2012 (to be published). IF - 0.219.
  8. Hadjiski M., L. Doukovska, St. Kojnov, Nonlinear Trend Analysis of Mill Fan System Vibrations for Diagnostics and Predictive Maintenance, International Journal of Electronics and Telecommunications (JET), Versita, Warsaw, Poland, ISSN 0867-6747, 2012, (to be published).
  9. Hadjiski M., L. Doukovska, P. Koprinkova-Hristova, Intelligent Diagnostic on Mill Fan System, Proc. of the 6th IEEE International Conference on Intelligent Systems IS'12, 6-8 September 2012, Sofia, Bulgaria (to be published).
  10. Iserman,R., Fault Diagnosis Systems, Springer, 2006.
  11. Krishnan,S. and R.Rangayyan, Denoising Knee Joint Vibration Signals Using Adaptive Time-Frequency representation, Canadian Conf. on Electrical and Computer Engineering, V.3, 1999.
  12. Li Hai Cheng, Qi Zhi, Application of Evidence Fusion Theory in Water Turbine Model, In: Intelligent Decision Technologies (J.Vatada, G.Phillips-Wren, C.Lakhimi, R.Howlett, Eds.), Springer, 2011.
  13. Rasovska,I., B.Chebel-Mollero and N.Zerhouni, A Case Elaboration Methodology for a Diagnostic and Repair Help System Based on Case-Based Reasoning, Proc. of AAAI, 2007 (www.aaai.org).
  14. Rafiee, J., F. Arvani, A. Narifi and M. H. Saheghi, Intelligent Condition Monitoring of a Gearbox Using Artificial Neural Networks, Mechanical Systems and Signal Processing, No. 21, 2007.
  15. Recèo-Garcia, J.A., B.Diaz-Agudo, A.A.Sanches-Ruiz, and P.A.Gonzales-Calero, Lessons Learned in the Development of a CBR Framework, Expert Update, Vol.10, ?1, 2010.
  16. Vachtsevanos,G., F.Lewis, M.Roemer, A.Hess and B.Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley, 2006.
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Paper Citation


in Harvard Style

Hadjiski M. and Doukovska L. (2012). CONSISTENT DATA AND DECISION FUSION OF HETEROGENEOUS INFORMATION DENOISING IN COMPLEX SYSTEMS DIAGNOSIS . In Proceedings of the First International Conference on Telecommunications and Remote Sensing - Volume 1: ICTRS, ISBN 978-989-8565-28-0, pages 163-169. DOI: 10.5220/0005415401630169


in Bibtex Style

@conference{ictrs12,
author={Mincho Hadjiski and Lyubka Doukovska},
title={CONSISTENT DATA AND DECISION FUSION OF HETEROGENEOUS INFORMATION DENOISING IN COMPLEX SYSTEMS DIAGNOSIS},
booktitle={Proceedings of the First International Conference on Telecommunications and Remote Sensing - Volume 1: ICTRS,},
year={2012},
pages={163-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005415401630169},
isbn={978-989-8565-28-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Telecommunications and Remote Sensing - Volume 1: ICTRS,
TI - CONSISTENT DATA AND DECISION FUSION OF HETEROGENEOUS INFORMATION DENOISING IN COMPLEX SYSTEMS DIAGNOSIS
SN - 978-989-8565-28-0
AU - Hadjiski M.
AU - Doukovska L.
PY - 2012
SP - 163
EP - 169
DO - 10.5220/0005415401630169