Analysis of Fault Signal in Gear Using Higher Order Time Frequency Analysis

  • Published : 1999.06.01

Abstract

Impulsive acoustic and vibration signals within gear are often induced by impacting of fault tooths in gear. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals then to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has found superior detection performance to second order Wigner distribution for typical impulsive signals in a condition monitoring application. These methods are also applied to data sets measured within an industrial gear box.

Keywords

References

  1. Machanical Systems and Signal Processing v.17 Early Detection of Gear Failure by Vibration Analysis-I and II Calculation of the Time-Frequency Distribution Wang, J.;Mcfadden, P. D.
  2. Phil. J. Res. v.35 The Wigner Distribution -A tool for time-frequency signal analysis Claasen, T.A.C.M.;Mecklenbrauker, W.F.G.
  3. IEEE Tran. ASSP v.37 Improved time-frequency representation of multiple component signal using exponential Kernel Choi, H.-I;Williams, W.J.
  4. Journal of Sound and Vibration Fault detection of rotating machinery using adaptive signal processing and time-frequency analysis Journal of Sound and Vibration Lee, S.K.;White, P.R.
  5. SPIE v.2027 Application of Cumulant TVHOS to the Analysis of Composite FM Sgnals in Multiplicative and Additive Noise Boashash, B.;Ristich, B.
  6. Proc. of ICASSP 1991 Toronto Canada Third-Order Wigner Distributions: Definition and Properties Swami, A.
  7. Proc. IEEE v.76 Introducing the Third-Order Wigner Distribution Gerr, N.L.
  8. IEEE Trans. Signal Processing v.41 Wigner-Higher-Order Moment Spectra Definition, Properties, Computation and Application to Transient Signal Analysis Fonollosa, J.R.;Nikias, C.L.
  9. IEEE Trans. on Signal Processing v.42 Decomposition of the Wigner-Ville Time-Frequency Distribution and Series Qian, S;Chen, D.
  10. Random Signals introduction to theory and application Cook, C.E.;Bernfeld, M.
  11. Proc. of ICASSP Representation of Multicomponent Sgnals Flandrin, P.
  12. Proc. of ICASSP Generalised Ambiguity Function Cohen, L.;Posch, T.E.
  13. IEEE Trans. ASSP v.38 The use of Cone-Shape Kernels for Generalished Time-Frequency Representations of Non-Sationary Signals Y. Zhao(et al)
  14. IEEE Trans. ASSP v.43 An Adaptive Optimal Kernel Time-Frequency Repressentation Jones, D.L;Baraniuk, R.G.
  15. IEEE Trans. on Signal Processing v.41 A signal dependent Time-Frequency Representation: Optimal kernel Design Baraniuk, R.G.;Jones, D.L.
  16. Ph. D. thesis, ISVR(Institute of Sound and Vibration Research) the University of Southampton Adaptive signal processing and higher order time frequency analysis and their application to acoustic and vibration signals of faults in rotating machinery Lee, S. K.
  17. Mechanical Systems and Signal Processing v.1 Examination of a Technique for the Early Detection of Failure in Gears by Signal Processing of the Time Domain Average of the Meshing Vibration McFadden, P.D.
  18. KASE 933753 v.1 no.2 A study Improvement of the Power Plant Vibration for the Noise Reduction and Sound Quality Improvement in the Compartment Journal of Korea Automotive Engineering Lee, S. K.
  19. SAE 930618 Identification of relation between crankshaft bendinf and interior noise A/T vehicle in idle state, New Engine Design and Engine Component Technology(SP-972) Lee, S. K
  20. Mechanical Systems and Signal Processing v.11 no.4 Higher order time-frequency analysis and its application to fault detection in rotating machinery Lee, S. K.;White, P. R.