Empirical Mode Decomposition(EMD), as a data driven and adaptive signal decomposition method, was widely utilized in fault feature extraction of bearing. Aim to solve the problems of mode mixing, end effect and the overshoot or undershoot brought by cubic spline interpolation.Meanwhile, considering thatthe rational Hermite interpolation method has a shape controllingparameter which can change the shape of interpolation curve. An improved EMD algorithm based on Particle Swarm Optimization (PSO) and rational Hermite interpolation is put forward. Firstly, the frequency bandwidth,as the evaluation function of PSO, is used to selected optimal IMF; Secondly, find out the optimal IMF from many different decomposition results and determine the optimal shape controlling parameters; Finally, the obtained IMF is optimal in each step of the decomposition result. Therefore,better adaptability and higher accuracy can be achieved. To verify the effectiveness of presented method, a simulation signal is processed by traditional EMD, EEMD and improved EMD, respectively. The comparison results show that the introduced algorithm can validly restrain the mode mixing and the obtained IMF has better consistency with the real component. Eventually, the improved EMD is applied to fault feature extractionof rolling bearing and compared with the traditional EMD, EEMD, the envelope spectra indicate thatthe proposed algorithm has better decomposability, ability of restraining interference and can extractmore fault information.
Key words
EMD /
rational Hermite interpolation /
PSO /
bearing /
fault feature extraction
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References
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