Minimum entropy deconvolution (MED) is an effective technique for detecting impulse-like signals such as bearing fault or gear fault signal, but there is still a deficiency in this method, that is, a parameter of the filter length in this method has to be set before using. Unfortunately, the selection of this parameter value can only be chosen through the human experience. In order to overcome this limitation, an optimal selection indicator based on Kurtosis, permutation entropy (PE) and signal energy is proposed in this study. By virtue of this indicator, the optimal filter length can be selected to filter the raw signal better. Then, an enhanced energy operator named envelope-derivation energy operator ( EDEO) is used to extract the fault characteristic frequency from the filtered signal. The experimental results show that, compared with the conventional methods, this proposed method can effectively extract the bearing fault characteristic frequency under harsh working conditions and obviously highlight the amplitude of the bearing fault frequency.