A novel bearing fault diagnosis method with feature selection and manifold embedded domain adaptation

Traditional fault diagnosis models assume that the training and test data sets have the same feature distribution, but in practice the distribution between the training and test sets varies considerably, making it difficult to achieve the desired fault diagnosis performance. Thus, a diagnosis method based on feature selection and manifold embedding domain adaptation is proposed in this paper. First, the signal is decomposed by variational modal decomposition to obtain multiple modal components, and the entropy, time domain and frequency domain features of each modal component are extracted to form a mixed feature set. Second, it proposes a feature evaluation index based on Fisher scores and feature domain differences to select features that are conducive to cross-domain fault diagnosis and transfer learning. Then, the geodesic flow core is constructed to learn the transformation feature representation in the Grassmann manifold space to avoid features are distorted. Finally, the domain adaptation is performed by minimizing the discrepancy in the joint probability distribution between the same category and maximizing the discrepancy between the different categories. Based on the results of multi-index experiments, the method in this paper is superior to other existing methods.