Adaptive generic prototype network with geodesic distance for cross-domain few-shot fault diagnosis

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Since the fault samples of equipment are limited and the working condition is often variable, it is valuable for researching the cross-domain few-shot fault diagnosis method to assure the safe operation of various machines. As the well-known few-shot classification approaches, the traditional prototype networks are difficult to process the complex sample distributions caused by variable operating condition data and the substantial distributional discrepancies between different machines, which seriously affects the accuracy of cross-domain fault diagnosis. To address these issues, this study proposes a new adaptive geodesic prototype network (AGPN), which can extract the category prototypes with enhanced adaptability and generalization capabilities. Firstly, a geodesic distance-driven learning strategy is developed to better measure the distance between complex samples in the embedding space. Secondly, an adaptive area prototype with a dynamic expansion coefficient is proposed, which allows for more flexible representation of different data categories. Furthermore, an adaptive momentum prototype method is put forward via a model-agnostic adaptive momentum factor, which can reduce the prototype oscillation during training and maximize the learning ability of model. The proposed AGPN is successfully applied to fault diagnosis across bearings with different operating conditions. Compared with the existing few-shot diagnosis methods, the proposed method possesses higher diagnostic accuracy and training stability, thus it is more suitable for cross-domain few-shot fault diagnosis.