Abstract: Aiming at the defects of fast ensemble empirical mode decomposition (FEEMD) and intrinsic mode functions (IMF) selection method in feature extraction, a fast complementary ensemble empirical mode decomposition (FCEEMD) composite screening based fault feature extraction method is proposed. Firstly, pairs of white noise with opposite signs are introduced to neutralize the residual noise in FEEMD and suppress the mode aliasing, and obtain a series of IMF. Secondly, a composite screening model is constructed based on the energy and correlation coefficients, and the reconstructed signal is constructed according to the effective IMF obtained by screening. Finally, the periodic pulse features contained in the reconstructed signal are extracted by Hilbert envelope demodulation to diagnose the bearing fault. The analysis results of Case Western Reserve bearing data show that the method can extract bearing fault features efficiently and accurately, which has reference significance and application prospects in the fault diagnosis of rotating machinery.

Key words: fast complementary ensemble empirical mode decomposition(FCEEMD), composite screening, feature extraction, fault diagnosis