Fault Feature Extraction of Mining Rolling Bearing Based on PSO-MCKD-Autogram
In order to analyze the operation state of mining rolling bearings and effectively extract the fault characteristics of mining rolling bearings,a parameter adaptive optimization Maximum Correlated Kurtosis De-convolution(MCKD)combined with Autogram is proposed based on Particle Swarm Optimization(PSO).MCKD combined with Autogram as a fault feature extraction algorithm for mining bearings.Firstly,based on the strong periodicity of the vibration signal,MCKD is used to preprocess the original signal to realize the noise reduction and enhancement of the signal;At the same time,in view of the MCKD parameter selection problem,PSO is con-structed to optimize the fitness function to obtain the suitable parameter combination[filter length L,deconvolution period T];Thereafter,Autogram is used to extract the features of the processed signal.Finally,the algorithm is validated by simulation signals and experimental signals from public datasets.The results show that the PSO-MCKD-Autogram algorithm can better overcome the influence of noise,and can effectively extract the fault features of mining bearings with certain robustness.The results can provide theoretical basis for condition monitoring and fault analysis of rolling bearings in mining.
mining rolling bearingmaximum correlation kurtosis deconvolutionAutogramfault diagnosis
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