Ensemble Empirical Mode Decomposition (EEMD) is a new noise-assisted data analysis (NADA) method. The effect of EEMD depends on two key parameters which are the amplitude of white noise and the ensemble times. However, the shortcoming of EEMD is that it lacks adaptability and reliability because these two key important parameters are obtained by experience and human intervention. An Improved Ensemble Empirical Mode Decomposition method is proposed in this paper, by adding white noise and ascertaining ensemble number adaptively. The criterion of adding white noise in Improved EEMD is established, by which a composite simulation signal could be adaptively and accurately decomposed into IMFs without mode mixing. The proposed method is applied to a gear fault detection of hot strip finishing mills. The result shows that Improved EEMD method successfully extracts the gear fault feature with high precise diagnosis results.

In order to protect the slider and disk from excessive wear, a numerical scheme is proposed for transient dynamics at slider/disk interface with the emphasis on the slider wear mechanism and the surface topology optimization of sub-ambient pressure slider. For each transient flying height and slider attitude, the modified Reynolds lubrication equation is solved by the control volume method to obtain the simultaneous pressure distribution in the slider surface. Then the dynamic flying attitude of the slider subject to the calculated pressure distribution can be solved by the Quasi-Newton iteration method at each time step. On the basis of above work, the optimization of the slider surface topology is implemented, and it’s concluded that slider crown has the most significant impact on the dynamic flying attitude and stability. Finally, a sub-ambient pressure slider is designed, which can effectively avoid the slider wear and severe fault of hard disk drive.