Identification-Based Condition Monitoring of Technical Systems

A Neural Network Approach
  • Anatoly Pashkevich
  • Gennady Kulikov
  • Peter Fleming
  • Mikhail Kazheunikau
Part of the Applied Optimization book series (APOP, volume 94)


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.

Key words

condition monitoring identification control charts neural networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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.Google Scholar
  2. 2.
    Basseville M. and Nikiforov I.V., 1993. Detection of abrupt changes: theory and applications, Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  3. 3.
    Chang S. I., 2000. An Integrated Scheme for Process Monitoring and Diagnosis. Proceedings of ASQC 49th Annual Quality Congress, Cincinnati, OH, 725–732.Google Scholar
  4. 4.
    Cheng C.S., 1997. A neural network approach for the analysis of control chart patterns. International Journal of Production Research, 35, 667–697.zbMATHCrossRefGoogle Scholar
  5. 5.
    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.Google Scholar
  6. 6.
    Frank P., 1990. Fault diagnosis in dynamic systems using analytical knowledge-based redundancy-a survey and some new results. Automatica, 26(3), 459–474.zbMATHCrossRefGoogle Scholar
  7. 7.
    Grant E.L. and Leavenworth R.S., 1996. Statistical quality control, New York: McGraw-Hill.Google Scholar
  8. 8.
    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.Google Scholar
  9. 9.
    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.Google Scholar
  10. 10.
    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.Google Scholar
  11. 11.
    Montgomery D.C., 1996. Introduction to Statistical Quality Control, John Wiley and Sons, Inc., New York.Google Scholar
  12. 12.
    Pham D.T. and Oztemel E., 1994. Control chart pattern recognition using learning vector quantization networks. International Journal of Production Research, 32, 721–729.Google Scholar
  13. 13.
    Tsung F., 2000. Statistical Monitoring and Diagnosis of Automatic Controlled Processes Using Dynamic PCA. International Journal of Production Research, 38, 625–637.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Anatoly Pashkevich
    • 1
  • Gennady Kulikov
    • 2
  • Peter Fleming
    • 3
  • Mikhail Kazheunikau
    • 1
  1. 1.Belarusian State University of Informatics and RadioelectronicsMinskBelarus
  2. 2.Ufa State Aviation Technical UniversityRussia
  3. 3.University of SheffieldUK

Personalised recommendations