Minimal entropy deconvolution (MED), an effective method widely used in the field of rotating machinery fault diagnosis, can extract weak fault impact components from noise. However, its effectiveness depends on the selection of filter length. Currently, no clear and effective method exists for the automatic selection of MED filter length, thus often requiring experience based human selection. Based on the MED algorithm and combining the autocorrelation function, a new method for MED optimal filter length selection is proposed. It constructs an energy target to measure the periodicity of the output signal and adaptively determine the filter length to improve the periodic pulse components in the weak fault signal, hence avoiding maximization of a single random pulse. The effectiveness of the method was verified in two cases:the rolling bearing test in the Cincinnati based test center; the fault test of an aero-engine rotor tester with casing simulating the case of the signal being far away from the rolling bearing. The results show that the proposed method can eliminate the effect of the transmission path and enhance the weak periodic impulses. Compared with those of the maximum correlated kurtosis deconvolution method, the diagnostic results are more advantageous.
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