Abstract
The diagnosis of bearing faults in rotating machines working with variable speed, such as wind turbines, gearboxes, and mine excavators, represents a challenge when using vibration analysis. In this paper, the feasibility of an optimized hybrid method based on Empirical Mode Decomposition (EMD) and Wavelet Multi-Resolution Analysis (WMRA) is checked for rolling bearing fault diagnostic by analyzing non-stationary vibration signals obtained from a variable speed rotating machine. An optimized EMD analysis called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is first used to decompose bearing signals. Amongst obtained Intrinsic Mode Functions (IMF), the one that has the highest kurtosis and covers the bearing natural frequency is chosen to be used for the next step which, given a signal, calculates its envelope by applying Hilbert Transform and then produces a new reconstructed signal using an Optimized WMRA. An Order Tracking (OT) algorithm is then applied on the envelope of the reconstructed signal to remove the effects of speed variation. An envelope order spectrum is finally calculated to bring out the fault characteristic order. The results show that the proposed hybrid approach have successfully highlighted the bearing faults in the non-stationary conditions, with both simulated and experimental signals.
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Bouhalais, M.L., Djebala, A., Ouelaa, N. et al. CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed. Int J Adv Manuf Technol 94, 2475–2489 (2018). https://doi.org/10.1007/s00170-017-1044-0
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DOI: https://doi.org/10.1007/s00170-017-1044-0