Effect of parameters setting on performance of discrete component removal (DCR) methods for bearing faults detection
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Abstract
Separation between non-deterministic and deterministic components of gearbox vibration signals has been considered as important signal processing step for rolling-element bearing fault diagnostics. In this paper, the performance of bearing fault detection after applying various discrete components removal (DCR) methods is quantitatively compared. Three methods that have become widely used, namely (i) time synchronous average, (ii) self adaptive noise cancellation (SANC) and (iii) cepstrum editing, were considered. The three DCR methods with different parameter settings have been applied to vibration signals measured on two different gearboxes. In general, the experimental results show that cepstrum editing method outperforms the other two methods.
How to Cite
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vibration, Bearing Faults, Discrete Component Removal (DCR)
Antoni, J., & Randall, R. (2004). Unsupervised noise cancellation for vibration signals: part ievaluation of adaptive algorithms. Mechanical Systems and Signal Processing, 18(1), 89–101.
Bogert, B. P., Healy, M. J., & Tukey, J. W. (1963). The quefrency alanysis of time series for echoes: Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking. In Proceedings of the symposium on time series analysis (pp. 209–243).
Bonnardot, F., El Badaoui, M., Randall, R., Daniere, J., & Guillet, F. (2005). Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Mechanical Systems and Signal Processing, 19(4), 766–785.
Gao, Y., & Randall, R. (1996). Determination of frequency response functions from response measurementsii. regeneration of frequency response from poles and zeros. Mechanical systems and signal processing, 10(3), 319–340.
Randall, R., & Sawalhi, N. (2011). A new method for separating discrete components from a signal. Sound and Vibration, 45(5), 6.
Randall, R., Sawalhi, N., & Coats, M. (2011). A comparison of methods for separation of deterministic and random signals. International Journal of Condition Monitoring, 1(1), 11–19.
Randall, R. B. (2011). Vibration-based condition monitoring: industrial, aerospace and automotive applications. John Wiley & Sons.
Sawalhi, N., & Randall, R. (2011). Signal pre-whitening using cepstrum editing (liftering) to enhance fault detection in rolling element bearings. In Proceedings of the 24 international congress on condition monitoring and diagnostic engineering management (comadem2011), may (pp. 330–336).
Widrow, B., Hoff, M. E., et al. (1960). Adaptive switching circuits.
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