A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution

Abstract

Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the objective function and solves the optimal filter by iteration. In addition, the filter length is not adaptive and needs to be determined artificially. This paper proposes a maximum kurtosis spectral entropy deconvolution (MKSED) method and applies it to bearing fault diagnosis. Considering that the kurtosis spectral entropy has the advantage of highlighting the continuous impact oscillation, the kurtosis spectral entropy is chosen as the objective function of deconvolution. At the same time, kurtosis spectral entropy is also used as the fitness function of improved local particle swarm optimization algorithm (LPSO), and the filter length is optimized by LPSO, which makes that MKSED adaptively determines the length of the filter while solving the deconvolution, so that it can accurately extract the continuous pulse signal. The results of the simulation signal analysis show that the proposed MKSED method is superior to MED, and the proposed method is applied to bearing fault diagnosis, which verifies its ability to extract continuous impact.

Keywords

• Minimum entropy deconvolution
• particle swarm optimization
• maximum kurtosis spectral entropy deconvolution
• fault diagnosis