Abstract
An integration method of variable precision rough set and neural network for fault diagnosis is presented and used in rotary machinery fault diagnosis. The method integrates the ability of variable precision rough set on reduction of diagnosis information system and that of neural network for fault classification. Typical faults of rotating machinery were simulated in our rotor test-bed. The power spectrum data are used as rotating machinery fault diagnosis signal. For inconsistent data and noise data in power spectrum, variable precision rough set model allows a flexible region of lower approximations by precision variables. By attribute reduction based on variable precision rough set, redundant attributes are identified and removed. The reduction results are used as the input of neural network. The diagnosis results show that the proposed approach for input dimension reduction in neural network is very effective and has better learning efficiency and diagnosis accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
An, A., Shan, N., Chan, C., Cercone, N., Ziarko, W.: Discovering Rules for Water Demand Prediction: An Enhanced Rough-set Approach. Engineering Applications in Artificial Intelligence 9(6), 645–653 (1996)
Beynon, M.: Reducts within the Variable Precision Rough Set Model: A Further Investigation. European Journal of Operational Research 134, 592–605 (2001)
Beynon, M.: An Investigation of β-reduct Selection within the Variable Precision Rough Sets Model. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 114–122. Springer, Heidelberg (2001)
Depold, H.R., Gass, F.D.: The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics. Journal of Engineering for Gas Turbines and Power, Transactions of the ASME 121, 607–612 (1999)
Hu, T., Lu, B.C., Chen, G.J.: A Gas Turbo Generator Fault Diagnosis New Approach Based on Rough Set Theory. Journal of Electronic Measurement and Instrument 15(1), 12–16 (2001)
Katzberg, J.D., Ziarko, W.: Variable Precision Extension of Rough Sets. Fundamental Informatics 27, 155–168 (1996)
Liu, H.J., Tuo, H.Y., Liu, Y.C.: Rough Neural Network of Variable Precision. Neural Processing Letters, Kluwer Academic Publishers 19, 73–87 (2004)
Liu, S.C., Liu, S.Y.: An efficient expert system for machine fault diagnosis. International Journal Advanced Manufacturing Technology 21, 691–698 (2003)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)
Pawlak, Z.: Rough sets theory and its applications to data analysis. Cybernetics and Systems 29, 661–668 (1998)
Paya, B.A., East, I.I., Badi, M.N.: Artificial Neural Network Based Fault Diagnostics of Rotating Machinery Using Wavelet Transforms as a Preprocessor. Mechanical Systems and Signal Processing ll, 751–765 (1997)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Ziarko, W.: Analysis of Uncertain Information in the Framework of Variable Precision Rough Sets. Foundations of Computing And Decision Sciences 18(3-4), 381–396 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Q., Yin, C., Li, Y. (2005). Application of Variable Precision Rough Set Model and Neural Network to Rotating Machinery Fault Diagnosis. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_61
Download citation
DOI: https://doi.org/10.1007/11548706_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
eBook Packages: Computer ScienceComputer Science (R0)