A small-sample bearing fault diagnosis method based on interpretable component energy features

Due to the weak fault signals of motor bearings and the extreme difficulty in obtaining sufficient data samples, the requirements for training fault diagnosis models cannot be met. Moreover, current machine learning-based fault diagnosis methods lack interpretability. To address this issue, this paper proposes a small-sample bearing fault diagnosis method based on interpretable component energy features (ICEF). By combining the component energy values of bearing vibration signal samples with interpretable artificial intelligence techniques, an interpretable feature selection method is proposed. This method extracts effective features of bearing faults as training features for the fault diagnosis model, enhancing the model's ability to learn and identify fault category characteristics. Finally, experiments demonstrate that the fault diagnosis accuracy reaches 99.5% and 97.5% when component energy is used as the learning feature. Moreover, by mixing data from two bearing datasets and dividing them into training and test sets at different ratios, an accuracy of 91.7% is achieved, validating the effectiveness of the proposed method under small-sample conditions.