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.
关键词
最大相关峭度解卷积/卷积神经网络/滚动轴承/故障诊断
Key words
Maximum Correlation Kurtosis Deconvolution/Convolutional Neural Network/Rolling Bearing/Fault Diagnosis
维普
万方数据