A Study on Fault Diagnosis of Rotating Machinery Combined Wavelet Transform with VMD

Abstract

Abstract Effective diagnosis of rotating machinery is difficult in view of the complex structure, weak early fault signals, non-stationary and non-linear vibration signals, and low signal-to-noise ratio. In this paper, a fault diagnosis method is proposed based on particle swarm optimization (PSO) and variational modal decomposition (VMD). Firstly, wavelet packet threshold is denoised on the signal, VMD is decomposed on the reconstructed signal, and PSO is optimized on the inherent mode function (IMF) obtained from decomposition so as to determine the best IMF function. Then Hilbert transform and envelope spectrum analysis are carried out on the IMF function, and the envelope spectrum analysis result is compared with theoretical calculation frequency to finally determine the fault type. The results indicate that this method can effectively reduce noise components in signals, extract weak fault information and realize fault diagnosis.