Retrospective Prototype Network Based on Center Difference Measure for Cross-Machine Few-Shot Fault Diagnosis

Metric-based meta-learning has gained extensive attention in recent years due to its rapid adaptability and strong generalization capability. However, most of the existing metric-based meta-learning methods overlook the intrinsic structures of data, and the similarity evaluation methods for the few-shot scenarios are scarce, which also need to be improved. Therefore, this article proposes a novel metric-based meta-learning method, named retrospective prototype network, for few-shot fault diagnosis across both machines and operating conditions. In this method, the retrospective prototype is developed, which utilizes the interclass variability and multidimensional correlation for accurately reflecting the complex class distributions while reducing the prototype oscillation. Moreover, considering the discrepancy between data intrinsic structures, a center difference measure is designed based on the difference between the central matrix of query sample and the prototype, thus it is more suitable for few-shot scenarios, where the high-dimensional covariance matrices are not exact and full-rank. This proposed method is successfully applied to cross-bearing few-shot fault diagnosis, and the comparative results demonstrate its superiority over the typical and advanced fault diagnosis methods.