Abstract
A novel identification-based technique for fault detection and condition monitoring of hydro- and electromechanical servomechanisms is proposed. It is based on neural network analyses of the control charts presenting behavior of the dynamic model parameters. There were derived analytical expressions that allow minimizing impact of the measurement errors on the identification accuracy. The proposed technique has been implemented in a software tool that allows automating the decision-making.
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
Anagun A. S., 1998. A neural network applied to pattern recognition in statistical process control. Proceedings of the 23rd Int. Conf. on Computers and Ind. Engineering, Chicago, IL, 185–188.
Basseville M. and Nikiforov I.V., 1993. Detection of abrupt changes: theory and applications, Englewood Cliffs, NJ: Prentice-Hall.
Chang S. I., 2000. An Integrated Scheme for Process Monitoring and Diagnosis. Proceedings of ASQC 49th Annual Quality Congress, Cincinnati, OH, 725–732.
Cheng C.S., 1997. A neural network approach for the analysis of control chart patterns. International Journal of Production Research, 35, 667–697.
Dash S. and Venkatasubramanian V., 2000. Challenges in the industrial applications of fault diagnostic systems. Proceedings of the conference on Process Systems Engineering, Keystone, Colorado, 785–791.
Frank P., 1990. Fault diagnosis in dynamic systems using analytical knowledge-based redundancy-a survey and some new results. Automatica, 26(3), 459–474.
Grant E.L. and Leavenworth R.S., 1996. Statistical quality control, New York: McGraw-Hill.
Hwarng H.B. and Hubele N.F., 1993. X-bar control chart pattern identification through efficient off-line neural network training. IIE Transactions, 25, 27–40.
Kulikov G.G., Breikin, T.V., Arkov V.Y. and Fleming, P.J., 1999. Real-time simulation of aviation engines for FADEC test-beds. Proceedings of the International Gas Turbine Congress, Kobe, Japan, 949–952.
Lucy-Bouler T.L., 1993. Application to forecasting of neural network recognition of shifts and trends in quality control data. Proceedings of WCNN’93—World Congress on Neural Networks, Portland, UK, vol. 1, 631–633.
Montgomery D.C., 1996. Introduction to Statistical Quality Control, John Wiley and Sons, Inc., New York.
Pham D.T. and Oztemel E., 1994. Control chart pattern recognition using learning vector quantization networks. International Journal of Production Research, 32, 721–729.
Tsung F., 2000. Statistical Monitoring and Diagnosis of Automatic Controlled Processes Using Dynamic PCA. International Journal of Production Research, 38, 625–637.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science + Business Media, Inc.
About this chapter
Cite this chapter
Pashkevich, A., Kulikov, G., Fleming, P., Kazheunikau, M. (2005). Identification-Based Condition Monitoring of Technical Systems. In: Dolgui, A., Soldek, J., Zaikin, O. (eds) Supply Chain Optimisation. Applied Optimization, vol 94. Springer, Boston, MA. https://doi.org/10.1007/0-387-23581-7_19
Download citation
DOI: https://doi.org/10.1007/0-387-23581-7_19
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23566-0
Online ISBN: 978-0-387-23581-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)