Abstract
In the field of vibration based condition monitoring a trusted symptom of a defective bearing is the observation of peaks, at characteristic frequencies, in the squared envelope spectrum (SES). If a machine is operating in a varying speed regime the SES is computed on the order tracked signal, i.e. the signal resampled at constant angular increments, and the SES can still be used for diagnostic. Despite its versatility a common problem with the SES is that peaks from other sources of vibrations, as for instance gears, can prevent the diagnosis of a defective bearing. Therefore pre-processing techniques are applied to the vibrational signal before the computation of the SES to enhance the signal from the bearings. Among these techniques cepstral pre-whitening (CPW) has gained much attention offering a remarkable capability of eliminating, in a blind way, both harmonics and modulation side-bands of the unwanted components. In the case of a varying speed regime the usual procedure consists of three steps: order track the signal, calculate the CPW, evaluate the SES. In this paper on the contrary the CPW is applied before the step of order tracking; therefore the proposed approach is: CPW the raw time signal, order tracking, evaluation of the SES. The remarkable observation is that for this approach the cepstrum does not present peaks at characteristic quefrencies, being the raw signal acquired in a varying speed regime. However this paper shows by means of numerical simulations and analysis of experimental data, that with the proposed methodology the masking components coming from the gears are suppressed and the signal from the defective bearing is enhanced.
References
Randall, R. B., & Sawalhi, N. (2011). A new method for separating discrete components from a signal. Sound and Vibration.
Randall, R. B., Sawalhi, N., & Coats, M. (2011). A comparison of methods for separation of deterministic and random signals. The International Journal of Condition Monitoring, 1(1), 11–19, June.
Sawalhi, N., & Randall, R. B. (2011). Signal pre-whitening for fault detection enhancement and surveillance in rolling element bearings. In Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. Cardiff, UK.
Borghesani, P., Pennacchi, P., Randall, R., Sawalhi, N., & Ricci, R. (2013). Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 36(2), 370–384.
Abboud, D., Eltabach, M., Antoni, J., & Sieg-Zieba, S. (2015). Envelope preprocessing techniques for rolling element bearing diagnosis in variable speed conditions. In The twelfth International Conference on Condition Monitoring (CM) and Machinery Failure Prevention Technologies (MFPT). Oxford, UK: The Oxford Hotel.
Borghesani, P., Ricci, R., Chatterton, S., & Pennacchi, P. (2013). A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions. Mechanical Systems and Signal Processing, 38(1), 23–35, July.
Randall, R., Smith, W., & Coats, M. (2014). Bearing diagnostics under widely varying speed conditions. In Proceedings of the 4th Conference in Condition Monitoring of Machinery in Non-stationary Operations. Lyon, France, 14–16, December.
Oppenheim, A., & Lim, J. (1981). The importance of phase in signals. Proceedings of the IEEE, 69(5), 529–541.
Aiger, D., & Talbot, H. (2010). The phase only transform for unsupervised surface defect detection. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 295–302). San Francisco, CA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Barbini, L., Eltabach, M., du Bois, J.L. (2018). Application of Cepstrum Prewhitening on Non-stationary Signals. In: Timofiejczuk, A., Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2016. Applied Condition Monitoring, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-61927-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-61927-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61926-2
Online ISBN: 978-3-319-61927-9
eBook Packages: EngineeringEngineering (R0)