Fault Diagnosis of Rolling Bearing Based on Adaptive MCKD and CNN
In order to solve the problem of the difficulty of extracting fault features under strong background noise and the tradi-tional methods relying on experience and knowledge,a rolling bearing fault diagnosis method based on adaptive maximum corre-lation kurtosis deconvolution(MCKD)and convolutional neural network(CNN)is proposed.Firstly,particle swarm optimiza-tion(PSO)is used to optimize the parameters of MCKD.Secondly,the rolling bearing fault signal is filtered to get the denoising signal.Finally,the de-noising signal is input into the constructed CNN model for training and testing,and the classification re-sult of bearing fault diagnosis is obtained.Through the test and evaluation of the fault data set of the bearing life test rig,the pro-posed method is compared with the CNN method without noise reduction,and it is verified that the method has high diagnostic accuracy.
Maximum Correlation Kurtosis DeconvolutionConvolutional Neural NetworkRolling BearingFault Diagnosis
万方数据
维普
