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Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling. The combination of Kernel Principal Component Analysis (KPCA) and K-Nearest Neighbor (KNN) is applied to fault diagnosis of bearing. In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of vibration signals to high dimensional feature space, and structure and statistics in the feature space to extract the feature vector from the fault signal with the principal component analytic method. Assessment method using the feature vector of the Kernel Principal Component Analysis, and then enter the sensitive features to K-Nearest Neighbor classification. The experimental results indicated that this method has good accuracy.

Rolling bearing fault signals are non-smooth, non-linear, and susceptible to background noise interference. A feature layer fusion model combining multi-sensor signals and parallel attention convolutional neural networks is proposed and applied to the fault diagnosis work of rolling bearings. First, a multi-channel parallel convolutional neural network model is constructed according to the number of sensors, and the multi-sensor signals are fed to each parallel channel separately. Second, due to the different strengths of shock features within each channel and signal, the attention mechanism is introduced into each parallel channel, the fault features with strong shock characteristics are extracted, and the feature extraction capability for different sensor signals is improved. Finally, the extracted feature information is fused in the concatenate layer, the fused features are input to the fully connected layer, and the diagnosis results are output through the Softmax layer. The experimental results show that the model can effectively fuse multi-sensor signal features, and its recognition accuracy is greatly improved over that of the single sensor, providing a feasible method for bearing fault diagnosis.

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