Abstract
The maintenance strategy develops quickly under the requirement of equipments’ near-zero-downtime running performance. Condition Based Maintenance (CBM) makes the maintenance strategy by detecting the equipment’s condition and corrects them before failure which attracts more attention. However, the equipments’ running process differs greatly. The parameters which can signify the faults onsets are also different. This paper attempts to find uniform rule for condition prediction. Artificial Neural Networks play more and more important roles in times series prediction which can achieve the desired output without exactly mathematical model. Application of Neural Networks to condition prediction is presented in this paper. For different concern of condition prediction, RBF Neural Network and Elman Neural Network are selected for the condition prediction which they both achieve good 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
Gang, Y., Hai, Q., Dragan, D., Jay, L.: Feature Signature Prediction of a Boring Process Using Neural Network Modeling with Confidence Bounds. J. Adv. Manuf. Technol. 30, 614–615 (2006)
Rangangath, K., Samuel, H.H., Verduin, W.H.: System Health Monitoring and Prognostics - a Review of Current Paradigms and Practices. J. Adv. Manuf. Technol. 28, 1012–1017 (2006)
Srinivas, K., Michael, R.B.: Methods for Fault Detection, Diagnosis, and Prognostics for Building Systems-A review. Part I, HVAC & R Research 11, 3–6 (2005)
Tse, P.W., Atherton, D.P.: Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks. J. Vibration and Acoustics 121, 355–361 (1999)
Li, R.H., Meng, G.X., Gao, N.K., Xie, H.K.: Combined Use of Partial Least-squares Regression and Neural Network for Residual Life Estimation of Large Generator Stator Insulation. J. Meas. Sci. Technol. 18, 2074–2075 (2007)
Mahanty, R.N., Dutta Guppta, P.B.: Application of RBF Neural Network to Fault Classification and Location in Transmission lines. IEE Proc. Transm. Distrib. 151, 201–204 (2004)
Song, Y.H., Xuan, Q.Y., Johns, A.T.: Protection Scheme for E H V Transmission Systems with Thyristor Controlled Series Compensation Using Radial Basis Function Neural Networks. Electr. Mach. Power Syst. 25, 553–565 (1997)
Gao, X.Z., Ovaska, S.J.: Genetic Algorithm Training of Elman Neural Network in Motor Fault Detection. Neural Comput. & Applic. 11, 37–39 (2002)
Elman, J.: Finding Structure in Time. Cognitive Science 14, 179–211 (1990)
Seker, S., Turkcan, E.A.E.: Elman’s Recurrent Neural Network Applications to Condition Monitoring in Nuclear Power Plant and RotatingMachinery. Engineering Applications of Artificial Intelligence 16, 647–656 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Liu, C., Jiang, D., Zhao, M. (2009). Application of RBF and Elman Neural Networks on Condition Prediction in CBM. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_90
Download citation
DOI: https://doi.org/10.1007/978-3-642-01216-7_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01215-0
Online ISBN: 978-3-642-01216-7
eBook Packages: EngineeringEngineering (R0)