Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution

Abstract

Blind deconvolution has been proved to be an effective method for fault detection since it can recover periodic impulses from mixed fault signals convoluted by noise and periodic impulses. As a new blind deconvolution technique, maximum cyclostationarity blind deconvolution (CYCBD) has great advantages over minimum entropy deconvolution (MED), maximum correlation kurtosis deconvolution (MCKD), and optimal minimum entropy deconvolution (MOMEDA) in processing bearing fault signals. However, CYCBD has the following two defects: Cyclic frequency needs to be determined in advance; The filter length of CYCBD affects its ability to recover impulses. The ability of CYCBD to recover the impulse will increase with the increase of filter length, but the signal will be distorted if the length is too large. Besides, the time cost will increase significantly after the length is increased. In this paper, an optimization strategy of CYCBD parameters is proposed, and then an adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed. Firstly, aiming at the determination of cyclic frequency, this paper proposes a cyclic frequency set estimation method based on autocorrelation function of morphological envelope, and the validity of the method is verified by simulation and experiment. Secondly, for the second problem, after considering the performance and time cost of CYCBD, the performance efficiency ratio index is proposed. Then, the equal-step search strategy is used to adaptively select the filter length. Finally, the effectiveness of the method is verified by simulation and experiment.