The PPCA—a new method to select features in process of rolling bearing fault diagnosing

In this study, a new method was mentioned out to optimize the feature space with P-PCA(Parts Principal Component Analysis), which needs to deal with the data of each fault categories with PCA firstly, and then rebuild the feature space with parts principal components which were got previously. And all this were based on PCA (Principal Component Analysis), which aim at feature selection and redundancy features eliminating. Then recognize the rolling bearing fault patterns based on artificial neural network. The result of experiment indicate that compared with the PCA, the P-PCA avoid the interfering between features which belong to different fault patterns. Which make new feature space contains more useful information, decline the training error rate of artificial neural network, and raise the speed and accuracy of fault pattern recognizing.