Fault diagnosis method of coal mine machinery gearbox

In order to solve problem that vibration signal of coal mine machinery gearbox contains a large number of noise interference components, which makes it difficult to extract fault characteristics of the gearbox, a fault diagnosis method of coal mine machinery gearbox based on particle swarm optimization variational mode decomposition(PSO-VMD) and minimum entropy deconvolution(MED) is proposed. Firstly, PSO algorithm is used to optimize search for punish coefficient and the number of components directly affecting decomposition effect in VMD, so as to obtain the optimal parameter combination to maximize decomposition performance. The optimized VMD method is applied to decompose gearbox vibration signal to obtain a series of intrinsic mode function(IMF) components. Then, IMF component with the highest correlation with original signal is denoised by MED method to highlight fault impact characteristics. Finally, Hilbert envelope demodulation is performed for IMF components after noise reduction, so as to extract fault characteristics. The experimental results show that the method can accurately extract fault characteristics and realize gearbox fault diagnosis.