Skip to main content

Fault detection with signal models

  • Chapter
Fault-Diagnosis Systems

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Chapter 8

  1. Akaike, H. A new look at the statistical model identification. IEEE Trans. on Automatic Control, 19(6):716–723, 1974.

    Article  MATH  MathSciNet  Google Scholar 

  2. Best, R. Wavelets: Eine praxisorientierte Einführung mit Beispielen. Teile 2 & 8. Technisches Messen, 67(4 & 11):182–187, 491–505, 2000.

    Google Scholar 

  3. Bogert, B., Healy, M., and Tukey, J. The quefrency analysis of time series for echoes. In Rosenblatt, M., editor, Proc. Symp. Time Series Analysis, pages 209–243. Wiley, 1968.

    Google Scholar 

  4. Box, G. and Jenkins, G. Time series analysis: forecasting and control. Holden-Day, San Francisco, 1970.

    MATH  Google Scholar 

  5. Brigham, E. The fast Fourier transform. Prentice Hall, Englewood Cliffs, 2nd edition, 1974.

    MATH  Google Scholar 

  6. Burg, J. A new analysis technique for time series data. In NATO Advanced Study Institute on Signal Processing with Emphasis on Underwater Acoustics, August 1968.

    Google Scholar 

  7. Cooley, J. and Tukey, J. An algorithm for the machine calculation of complex fourier series. Math. of Computation, 19:297–301, 1965.

    Article  MATH  MathSciNet  Google Scholar 

  8. Edward, J. and Fitelson, M. Notes on maximum entropy processing. IEEE Trans. Inform. Theory, IT-19:232–234, 1973.

    Article  Google Scholar 

  9. Ericsson, S., Grip, N., Johannson, E., Persson, L., Sjöberg, R., and Strömberg, J. Towards automatic detection of local bearing defects in rotating machines. Mechanical Systems and Signal Processing, 9:509–535, 2005.

    Article  Google Scholar 

  10. Friedmann, A. An introduction to linear and nonlinear systems and their relation to machinery faults. www.DLIengineering.com, 2001.

    Google Scholar 

  11. Führer, J., Sinsel, S., and Isermann, R. Erkennung von Zündaussetzern aus Drehzahlsignalen mit Hilfe eines Frequenzbereichsverfahrens. Proc. 13. Tagung Elektronik im Kraftfahrzeug, Essen, 1993.

    Google Scholar 

  12. Hänsler, E. Statistische Signale — Grundlagen und Anwendungen. Springer, Berlin, 3rd edition, 2001.

    Book  MATH  Google Scholar 

  13. Harris, T. Rolling bearing analysis. J. Wiley & Sons, New York, 4th edition, 2001.

    Google Scholar 

  14. Hess, W. Digitale Filter. Teubner Studienbücher, Stuttgart, 1989.

    Google Scholar 

  15. Hippenstiel, R. Detection theory. CRC Press, Boca Raton, 2002.

    Google Scholar 

  16. Isermann, R. Digitale Regelsysteme, volume 1 &, 2. Springer, Berlin, 1988.

    Google Scholar 

  17. Isermann, R. Identifikation dynamischer Systeme. Springer, Berlin, 1992.

    Google Scholar 

  18. Isermann, R. Fault diagnosis of technical processes — applications. Springer, Heidelberg, 2006.

    Google Scholar 

  19. Isermann, R., Lachmann, K.-H., and Matko, D. Adaptive control systems. Prentice Hall International UK, London, 1992.

    MATH  Google Scholar 

  20. Janik, W. Fehlerdiagnose des Außenrund-Einstechschleifens mit Prozeß-und Signalmodellen, volume Fortschr.-Ber. VDI Reihe. VDI Verlag, Düsseldorf, 1992.

    Google Scholar 

  21. Janik, W. and Fuchs. Process-and signal-model based fault detection of the grinding process. In Prepr. IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), volume 2, pages 299–304, Baden-Baden, Germany, September 1991.

    Google Scholar 

  22. Janik, W. and Isermann, R. Signal model-based diagnosis system for the supervision of periodically and intermittant working machines tools. In Proc. 11th IFAC World Congress, volume 1, pages 130–134, Tallinn, USSR, 1990.

    Google Scholar 

  23. Kammeyer, K. and Kroschel, K. Digitale Signalverarbeitung: Filterung und Spektralanalyse. Teubner, Stuttgart, 3rd edition, 1996.

    Google Scholar 

  24. Kay, S. Modern spectral estimation-theory and applications. Prentice Hall, Englewood Cliffs, 1987.

    Google Scholar 

  25. Kolerus, J. Zustandsüberwachung von Maschinen. expert Verlag, Renningen-Malmsheim, 2000.

    Google Scholar 

  26. Makhoul, J. Linear prediction: a tutorial review. Proc. of IEEE, 63:561–580, 1975.

    Article  Google Scholar 

  27. Marple, S. Digital spectral analysis with applications. Prentice Hall, Englewood Cliffs, 1987.

    Google Scholar 

  28. Meyer-Bäse, U. Digital signal processing with field programmable gate arrays. Springer, Berlin, 2004.

    Book  MATH  Google Scholar 

  29. Mitra, S. Digital signal processing: a computer-based approach. McGraw Hill & Irwin, Boston, 2nd edition, 2001.

    Google Scholar 

  30. Muffert, K.-H. Ursachen und Beispiele von Schäden an Verbrennungsmotoren. Der Maschinenschaden, 53:95–102, 1980.

    Google Scholar 

  31. Neumann, D. Fault diagnosis of machine-tools by estimation of signal spectra. In Preprints IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), volume 1, pages 73–78, Baden-Baden, Germany, September 1991.

    Google Scholar 

  32. Neumann, D. Analyse periodischer Signale zur Fehlererkennung. In Isermann, R., editor, Überwachung und Fehlerdiagnose, pages 43–71. VDI, Düsseldorf, 1994.

    Google Scholar 

  33. Neumann, D. and Janik, W. Fehlerdiagnose an spanenden Werkzeugmaschinen mit parametrischen Signalmodellen von Spektren. In VDI-Schwingungstagung, Mannheim, Germany, 1990.

    Google Scholar 

  34. Nussbaumer, H. Fast Fourier transform and convolution algorithms. Springer, Heidelberg, 1981.

    Book  MATH  Google Scholar 

  35. Oppenheim, A., Schafer, R., and Buck, J. Discrete-time signal processing. Prentice Hall, Englewood Cliffs, 2nd edition, 1999.

    Google Scholar 

  36. Pandit, S. and Wu, S.-M., editors. Time series and system analysis with applications. Wiley, New York, 1983.

    MATH  Google Scholar 

  37. Papoulis, A. Probability, random variables, and stochastic processes. McGraw-Hill, New York, 2nd edition, 1994.

    Google Scholar 

  38. Platz, R. Untersuchungen zur modellgestützten Diagnose von Unwuchten und Wellenrissen in Rotorsystemen, volume Fortschr.-Ber. VDI Reihe 11, 325. VDI Verlag, Düsseldorf, 2004.

    Google Scholar 

  39. Platz, R., Markert, R., and Seidler, M. Validation of online diagnosis of malfunctions in rotor systems. In Trans. 7th ImechE-Conf. on Vibrations in Rotating Machines, pages 581–590, University of Nottingham, 2000.

    Google Scholar 

  40. Porat, B. A course on digital signal processing. J. Wiley & Sons Inc., New York, 1997.

    Google Scholar 

  41. Press, W., Flannery, B., Teukolsky, W., and Vetterling, S. Numerical recipes in C. Cambrigde University Press, Cambridge, 1988.

    MATH  Google Scholar 

  42. Qian, S. and Chen, D. Joint time-frequency analysis: methods and applications. Prentice Hall, Upper Saddle River, 1996.

    Google Scholar 

  43. Randall, R. Frequency analysis. Bruel & Kjaer, Naerum, 3rd edition, 1987.

    Google Scholar 

  44. Ribbens, W. and Rizzoni, G. Onboard diagnosis of engine misfires. In Proc. SAE 90, number SAE 901768, Warrendale, USA, 1990.

    Google Scholar 

  45. Schüßler, H. Digitale Signalverarbeitung 1-Analyse diskreter Signale und Systeme. Springer, Berlin, 4th edition, 1994.

    Google Scholar 

  46. Stearns, S. Digital signal analysis. Hayden Book Company, Rochelle Park, 1975.

    MATH  Google Scholar 

  47. Stearns, S. and Hush, D. Digital signal analysis. Prentice Hall, Englewood Cliffs, 1990.

    Google Scholar 

  48. Ulrych, T. and Bishop, T. Maximum entropy spectral analysis and autoregressive decomposition. Reviews of Geophysics and Space Physics, 13(February):183–200, 1975.

    Google Scholar 

  49. Williams, A. and Taylor, F. Electronic filter design handbook. McGraw Hill, 3rd edition, 1995.

    Google Scholar 

  50. Willimowski, M. Verbrennungsdiagnose von Ottomotoren mittels Abgasdruck und Ionenstrom. Shaker Verlag, Aachen, 2003.

    Google Scholar 

  51. Willimowski, M., Füssel, D., and Isermann, R. Diagnose von Verbrennungsaussetzern in Ottomotoren durch Messung des Abgasdrucks. Motortechnische Zeitschrift — MTZ, 10:654–663, 1999.

    Google Scholar 

  52. Willimowski, M. and Isermann, R. A time domain based diagnostic system for misfire detection in spark-ignition engines by exhaust-gas pressure analysis. In SAE 2000 World Congress, volume SP-1501, pages 33–43, Detroit, MI, USA, March 2000.

    Google Scholar 

  53. Wirth, R. Maschinendiagnose an Industriegetrieben — Grundlagen. Antriebstechnik, 37(10 & 11):75–80 & 77–81, 1998.

    Google Scholar 

  54. Wowk, V. Machinery vibrations. McGraw Hill, New York, 1991.

    Google Scholar 

  55. Zoubir, A. and Iskander, D. R. Bootstrap techniques for signal processing. University Press, Cambridge, 2004.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Isermann, R. (2006). Fault detection with signal models. In: Fault-Diagnosis Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-30368-5_8

Download citation

  • DOI: https://doi.org/10.1007/3-540-30368-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24112-6

  • Online ISBN: 978-3-540-30368-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics