Fault diagnosis of rolling bearings with noise signal based on modified kernel principal component analysis and DC‐ResNet

Abstract

Abstract In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis, a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis (MKPCA) and the residual network with deformable convolution (DC‐ResNet) is innovatively proposed. Firstly, the Gaussian noise with different signal‐to‐noise ratios (SNRs) is added to the data to simulate the different degrees of noise in the actual data acquisition process. The MKPCA is used to project the fault signal with different SNRs in the kernel space to reduce the data dimension and eliminate some noise effects. Finally, the DC‐ResNet model is used to further filter the noise effects and fully extract the fault features through the training of the preprocessed data. The proposed algorithm is tested on the Case Western Reserve University (CWRU) and Xi'an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU‐SY) bearing data sets with different SNR noise. The fault diagnosis accuracy can reach 100% within 30 min, which has better performance than most of the existing methods. The experimental results show that the algorithm has an excellent effect on accuracy and computation complexity under different noise levels.

Keywords

• artificial intelligence
• classification
• deep learning
• fault feature extraction