When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks.
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