Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation

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.

Automated summary

This paper proposes a method based on a stacked sparse autoencoder combined with a softmax classifier for fault diagnosis of rolling bearings. The method extracts frequency-domain features of vibration signals using a stacked sparse autoencoder and utilizes an improved K-fold cross-validation to obtain pre-train set, train set, and test set. The performance of the model is evaluated based on accuracy, macro-precision, macro-recall, and macro-F1 score. The proposed model is validated with high accuracy using data from Case Western Reserve University and XJTU-SY.