Fault Diagnosis of Connecting Rod Bearing Based on MCKD-HED-CNN
JIA Jide1,2,SHEN Yang1,XU Cailian1
Author information+
（1.The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province,Xiamen Institute of Technology,Xiamen 361021,China;
2.State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China）
Aiming at the difficult fault diagnosis of connecting rod bearing under strong background noise, the fault diagnosis method of MCKD-HED-CNN was proposed. Firstly, the maximum correlation kurtosis deconvolution(MCKD) algorithm was used to reduce noise and enhance the periodic impact caused by fault. Secondly, the Hilbert envelope demodulation(HED) was used to further enhance the periodic impact. Finally, the fault features were mapped to the polar map by the symmetric point mode(SPD) and the SDP image was input into CNN network for training to establish the fault diagnosis model of connecting rod bearing. The results show that the method can effectively diagnose the fault of connecting rod bearing, and the diagnosis accuracy of CNN training samples and test samples is 100%.
