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Recently, cyclostationary blind deconvolution (CYCBD) is often used in gearbox fault feature extraction, and it is more effective in recovering single periodic pulse. However, in the engineering application of CYCBD, the filter length ( $L$ ) and cycle frequency ( $\alpha$ ) need to be verified and set through a large number of experiments, and the efficiency is very low; Moreover, the effect is not good when it is used to extract composite fault features under the background of strong noise. In order to overcome the above limitations, empirical mode decomposition (EEMD) is used to preprocess the composite fault. EEMD can remove the high-frequency noise and weak correlation components in the sampled signal. The strong correlation component is reconstructed to obtain the modal function closer to the fault frequency. The chimpanzee intelligent algorithm is applied to the determination of $L$ and $\alpha $ of CYCBD by optimization to form an adaptive CYCBD. Adaptive CYCBD takes the dispersion entropy of envelope spectrum as the fitness function of chimpanzee optimization algorithm (CHOA), and finds the optimal $L$ and $\alpha $ through iteration. The optimal parameter value is applied to CYCBD, and the reconstructed modal function is deconvoluted to obtain the optimal inverse filter, so as to accurately separate the fault characteristic components. Simulation and experimental results show that this method is effective for gearbox composite fault diagnosis and extraction under strong noise background.

To detect the rolling bearing fault of a wind turbine at incipient injury phase, a novel method combining adaptive cyclostationary blind deconvolution (ACYCBD) with instantaneous energy slice bispectrum (IESB) is proposed. Cuckoo search algorithm (CSA) is fused with cyclostationary blind deconvolution (CYCBD) to optimize cyclic frequency and filter length parameters, and the satisfied processing result is able to adaptively use this developed ACYCBD method. Besides, fusing the complementary superiorities of frequency weighted energy operator and slice bispectrum, a new frequency domain analysis method named IESB is put forward to identify the characteristic frequency components. During fault diagnosis, the deconvolution signal with higher signal to noise ratio is first separated from the collected signal by applying ACYCBD method. However, owing to the intense background noises, external interferences still remain in the deconvolution signal. Then the IESB is further used as a post-processing method, the residual noises are suppressed effectively, and the characteristic spectral lines are highlighted immensely. In the end, the incipient fault injury of wind turbine bearing can be diagnosed by analyzing the acquired instantaneous energy slice bispectrum. The analyzed results of wind turbine bearing fault signals from the experimental rig and the actual engineering testing field demonstrate the feasibility and the superiority of this novel detection method. [ABSTRACT FROM AUTHOR]