A composite fault diagnosis method of gearbox based on an enhanced deconvolution algorithm

Abstract

Aiming at the problem that the feature components that can reflect the gearbox system state are often interfered by noise and harmonics, when gearbox composite fault diagnosis is performed under strong background noise in large rotating machinery, which makes the extraction of fault feature signals limited. A composite fault diagnosis method based on singular value negentropy (SVN) and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) is proposed. First, the original composite fault signal is divided into different frequency bands using the 1/3 binomial tree strategy to reduce the noise impact and harmonic interference. Then the SVN of all frequency bands is calculated to construct the ordering kurtosis spectrum and the optimal frequency band is selected on the basis of considering both the periodicity and impulsivity of the fault signal. Then the multipoint kurtosis(MK) of the optimal frequency band is calculated and according to the extracted fault impulsive periodicity components using the MOMEDA algorithm deconvolute the optimal frequency band signal. Finally, the type of fault is judged by analyzing the frequency components with prominent amplitude in the mutual correlation spectrum. The analysis results of the experimental platform show that the method can effectively extract the fault characteristics disturbed by strong noise and realize the accurate diagnosis of Composite faults in gearbox.