Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault

Abstract

Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications. The minimum entropy deconvolution (MED) and the optimal minimum entropy deconvolution adjusted (OMEDA) are susceptible to extreme values. Furthermore, maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) are required prior knowledge of faults. On the basis of the periodicity and impact of bearing fault signals, a new deconvolution algorithm, namely one based on maximum correlation spectral negentropy (CSNE), which adopts the particle swarm optimization (PSO) algorithm to solve the filter coefficients, is proposed in this paper. Verified by the simulated vibration model signal and the experimental simulation signal, the PSO–CSNE algorithm proposed in this paper overcomes the influence of harmonic signals and random pulse signals more effectively than other blind deconvolution algorithms when prior knowledge of the fault is unknown.

Keywords

• blind deconvolution
• correlation spectral negentropy
• particle swarm optimization algorithm
• harmonic interference
• random pulse
• feature extraction