Multi-scale dynamic graph mutual information network for planet bearing health monitoring under imbalanced data

In engineering, imbalanced data collected from planet bearings causes most intelligent models to shrink the decision boundary of minor classes and degrade diagnostic accuracy. Different from these models under the assumption of data balance, graph-based methods focus on the relationship between data to alleviate the issue of data imbalance, but they have restrictions on single-feature propagation and only rely on the feature extraction capability of convolutional operations. As such, a multi-scale dynamic graph mutual information network (MDGMIN) is proposed for the health monitoring of planet bearings with imbalanced data. First, a dual spatial-temporal graph generation algorithm is designed to construct dynamic and distance graphs via the gated convolution in the temporal dimension and the cosine similarity and Top-k sorting mechanism in the spatial dimension. Second, multi-scale dynamic edge graph convolutional layers are constructed to extract specific and similar features, and they are weighted fused via an attention mechanism. Finally, mutual information learning is developed to foster the model in capturing graph features in-depth through commonality and discrepancy constraints, and a new loss-driven function based on two constraints is proposed to update the training objective. Experimental analysis on an imbalanced planet bearing dataset verifies that the developed MDGMIN reaches the diagnostic accuracy of 92.80%, exceeding that of state-of-the-art methods on the dataset with an imbalanced ratio of 20:1. In addition, the generalizability of the MDGMIN is validated in another bearing dataset from the planetary gearbox.