Abstract
Many signal processing algorithms have been developed recently for mechanical fault detection in machinery diagnostics. Most of those are based on the assumption of stationarity but in many cases fault characteristics appear in non-stationary form e.g. impact excitation. Thus strong non-stationary events can appear in a local time period, eg. one revolution. The analysis of non-stationary signals calls for specific techniques which go beyond the classical Fourier approach. The past ten years have seen major developments in the area of time-variant analysis. Unfortunately, machinery diagnostics have not yet received major benefits from these developments and little attention has been paid to time-dependent methods. These developments can be classified into three major groups [1, 2]: time-dependent models, time-frequency distributions and time-scale methods as indicated in Figure 1.
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
Flandrin P. Some aspects of non-stationary signal processing with emphasis on time-frequency and time-scale methods. In Combes J M, Grossmann A, and Tchamitchian Ph, editors, Wavelets. Time-Frequency Methods and Phase Space. Springer-Verlag, Berlin, 1989.
Priestley M B. Non-linear and Non-stationary Time Series Analysis. Academic Press, London, 1988.
Allen J B. Short term spectral analysis, synthesis, and modification by discrete fourier transform. IEEE Trans. on Acoust., Speech and Signal Proc., ASSP–25(3): 235–238, 1977.
Kim Y H, Lim B D, and Cheoung W S. Fault detection in a ball bearing system using a moving window. Mechanical Systems and Signal Processing, 5 (6): 461–473, 1991.
Cohen L. Generalized phase-space distribution functions. J. Math. Phys., 7 (5): 781–786, 1966.
Cohen L. Time-frequency distributions–a review. Proceedings of the IEEE, 77 (7): 941–981, 1989.
Boashash B. Time-frequency signal analysis. In S Haykin, editor, Advances in Spectrum Analysis and Array Processing. vol I., Advanced Reference Series. Engineering. Prentice Hall, Englewood Cliffs, New Jersey, 1991.
Boashash B, editor. Time-Frequency Signal Analysis. Methods and Applications. Longman Cheshire, Melbourne, 1992.
Claasen T ACM and Mecklenbrauker W F G. The wigner distribution–a tool for time-frequency analysis. part 1: Continuous time signals. Philips J. Res., 35 (3): 217–250, 1980.
Claasen T ACM and Mecklenbrauker W F G. The wigner distribution–a tool for time-frequency analysis. part 2: Discret time signals. Philips J. Res., 35 (4/5): 276–300, 1980.
Claasen T A C M and Mecklenbrauker W F G. The wigner distribution–a tool for time-frequency analysis. part 3: Relations with other time-frequency transformations. Philips J. Res., 35 (6): 372–389, 1980.
Forrester B D. Use of the wigner-ville distribution in helicopter fault detection. In Proc. ASSPA 89, pages 77–82, 1989.
Forrester B D. Analysis of gear vibration in the time-frequency domain. In Proceedings of the 44th Meeting of the Mechanical Failures Prevention Group of the Vibration Institute, pages 225–239, Virgina Beach, Virginia, 1990.
Forrester B D. Time-frequency domain analysis of helicopter transmission vibration. Technical Report 180, Aeronautical Research Laboratory, ARL Propulsion, Melbourne, 1990.
Forrester B D. Time-frequency analysis in machine fault detection. In Boashash B, editor, Time-Frequency Signal Analysis. Longman Cheshire, Melbourne, 1992.
introduction to the wigner-ville distribution. Technical Report Report No OUEL 1859/90, University of Oxford. Department of Engineering Science., Oxford, 1990.
the weighted wigner-ville distribution. Technical Report Report No OUEL 1891/91, University of Oxford. Department of Engineering Science., Oxford, 1991.
Meng Q and Qu L. Rotating machinery fault diagnosis using wigner distribution. Mechanical Systems and Signal Processing, 5 (3): 155–166, 1991.
McFadden P D and Wang W J. Analysis of gear vibration signatures by the weighted wigner-ville distribution. In Proceedings of the Fifth International Conference on Vibrations in Rotating Machinery. University of Bath, IMechE, September 1992.
Garudadri H, Beddoes M P, Benguerel A P, and Gilbert J H V. On computing the smoothed wigner distribution. In Proc of IEEE ICASSP 87, pages 15211524, 1987.
Choi H I and Williams W J. Improved time-frequency representation of multi-component signals using exponential kernels. IEEE Trans. on Acoust., Speech and Signal Proc., ASSP-37(6): 862–871, 1989.
Gabor D. Theory of communication. J. Inst. Elect r. Eng., 93 (3): 429–457, 1946.
Helstrom C W. An expansion of a signal in gaussian elementary signals. IEEE Trans. on Inf. Theory, IT-12: 81–82, 1966.
Grossmann A, Kronland-Martinet R, and Morlet J. Reading and understanding continuous wavelet transform. In Combes J M, Grossmann A, and Tchamitchian Ph, editors, Wavelets. Time-Frequency Methods and Phase Space. Springer-Verlag, Berlin, 1989.
Kronland-Martinet R and Grossmann A. Application of time-frequency and time-scale methods (wavelet transform) to the analysis, synthesis and transformation of natural sounds. In Piccialli A Deppoli G and Roads C, editors, Representation of Musical Signals. The MIT Press, Cambridge, Massachusetts, 1991.
Morlet J. Sampling theory and wave propagation. In Proc. 51st Ann. Meeting Soc. Explor. Geophys., Los Angeles, 1980.
Chui Ch K. An Introduction to Wavelets. Wavelets Analysis and its Applications. vol 1. Academic Press, Inc., Boston, 1992.
Chui Ch K, editor. Wavelets: A Tutorial in Theory and Applications. Wavelets Analysis and its Applications. vol 2. Academic Press, Inc., Boston, 1992.
Daubechies I. The wavelet transform - a method for time-frequency localization. In Haykin S, editor, Advances in Spectrum Analysis and Array Processing. vol I., Advanced Reference Series. Engineering. Prentice Hall, Englewood Cliffs, New Jersey, 1991.
Kronland-Martinet R and Morlet J. Analysis of sound through wavelet transforms. International Journal of Pattern Recognition and Artificial Intelligence, 1 (2): 273–302, 1987.
Tuteur F B. Wavelet transformations in signal detection. In Combes J M, Grossmann A, and Tchamitchian Ph, editors, Wavelets. Time-Frequency Methods and Phase Space. Springer-Verlag, Berlin, 1989.
Staszewski W J and Tomlinson G R. Application of the wavelet transform to fault detection in a spur gear. In preparation.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1993 Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden
About this chapter
Cite this chapter
Staszewski, W.J., Tomlinson, G.R. (1993). Time-variant Methods in Machinery Diagnostics. In: Safety Evaluation Based on Identification Approaches Related to Time-Variant and Nonlinear Structures. Vieweg+Teubner Verlag. https://doi.org/10.1007/978-3-322-89467-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-322-89467-0_5
Publisher Name: Vieweg+Teubner Verlag
Print ISBN: 978-3-528-06535-5
Online ISBN: 978-3-322-89467-0
eBook Packages: Springer Book Archive