Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation
Junxiang Wang, Changshu Zhan, Di Yu, Qiancheng Zhao, Zhijie Xie
Abstract
Abstract Since rolling bearings determine the stable operation of industrial equipment, it is necessary to diagnose thir faults. To improve fault diagnosis accuracy, this paper proposes a method based on a stacked sparse autoencoder (SSAE) combined with a softmax classifier. First, SSAE is used to extract the frequency-domain features of vibration signals. Then, an improved K-fold cross-validation is employed to obtain the features’ pre-train set, train set, and test set. Finally, the SSAE-model is constructed via the pre-train set, while the tuned model is built via the train set. The model performance is evaluated based on accuracy, macro-precision, macro-recall, and macro-F1 score. The proposed model is validated by the Case Western Reserve University and XJTU-SY data with 99.15% and 100% accuracy, respectively.