Neighborhood graph embedding interpretable fault diagnosis network based on local and non-local information balanced under imbalanced samples

Currently, the monitoring of the health status of mechanical systems is becoming more and more critical, and the actual monitoring data is massive and high-dimensional, and these data are characterized mainly by imbalance. To solve the problem of low fault recognition rate caused by data imbalance, this article proposed a novel local and non-local information balanced neighborhood graph embedding interpretable deep autoencoder (LGBNGEDAE) method for rotating machinery fault diagnosis. Specifically, the local and non-local neighborhood information graph is embedded into the original objective function of the deep autoencoder to smooth the manifold structure of the data in LGBNEDSAE, which endows the deep learning model with better data learning capability and robustness of feature extraction as well as more reasonable network interpretability, making it more suitable for feature learning and classification of unbalanced data. Further, the method’s excellent fault diagnosis capability and generalization performance are verified on two opposed experimental cases of gearboxes. Compared with other deep learning architectures and shallow learning models, the method performs better in the face of unbalanced datasets.