Fault Diagnosis of the Sliding Bearings for Induction Motors Based on PSO-VMD and EWT
Aiming at the difficulty of the fault diagnosis of sliding bearings in induction motors,a fault diagnosis meth-od of sliding bearings based on frequency-domain integration,variable mode decomposition(VMD),and empirical wavelet transform(EWT)is proposed.The fault diagnosis of an actual sliding bearing of a motor is taken as an example.Firstly,the displacement signal is obtained through frequency-domain integration.Through analyzing the time-domain and frequency-domain characteristics of the displacement signal,the two potential faults,the friction fault and the misalignment fault,are diagnosed.However,the shaft orbit diagram is too chaotic to provide any affirmative conclusion.Then,the VMD algorithm optimized by the particle swarm optimization(PSO)and the wavelet threshold denoising method are applied to remove the noise in the original displacement signal.The principal frequency components of the displacement signal obtained by the EWT algorithm are obtained and reconstructed,and the shaft orbit is redrawn.The analysis show that the purified shaft or-bits is sharp and can provide evident characteristics.It can be concluded according to the shaft orbit that the motor has the friction-bearing misalignment coupled fault.Finally,compared with the ensemble empirical mode decomposition(EEMD)and some other methods,the proposed method can obtain sharp shaft orbit diagrams,improve the shaft orbit,and is benefi-cial for the fault diagnosis of the sliding bearings in induction motors.
万方数据
维普
