多源信息融合驱动的元类增量智能迁移诊断方法

During the full lifecycle service process of high-end equipment, the continuous emergence of new fault modes and their evolution poses great challenges to lifelong intelligent diagnosis capability of intelligent fault diagnosis models, lifelong intelligent diagnosis technology driven by class-incremental learning has been regarded as a crucial support for ensuring the operational safety of high-end equipment throughout its life cycle. To address the limitations of existing class-incremental learning methods, such as restricted diagnostic performance in class-incremental transfer scenarios, susceptibility to catastrophic forgetting, and insufficient model generalization, proposes a meta-class-incremental intelligent transfer diagnosis method driven by multi-source information fusion. The incorporation of continuous wavelet transform and RGB multi-channel information fusion mechanism is employed to integrate time-frequency features and spatial information from multi-sensor data, enhancing the feature representation capability of fault samples in class-incremental transfer diagnosis scenarios. An enhanced knowledge distillation method is designed and synergized with a memory replay strategy to effectively mitigate the catastrophic forgetting issue in class-incremental transfer diagnosis scenarios. A novel meta-class-incremental parameter learning strategy is developed by incorporating meta-learning into the class-incremental learning framework to effectively improve the model generalization in class-incremental transfer diagnosis scenarios. Finally, the effectiveness and superiority of the proposed method are validated using a planetary transmission fault dataset. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 96.21% and an average forgetting rate of only 1.76% across different class-incremental transfer diagnosis scenarios, thereby verifying its superior generalization ability and strong resistance to forgetting for achieving class-incremental transfer diagnosis. Compared to state-of-the-art methods, the proposed method exhibits advantageous class-incremental transfer diagnostic performance, providing a novel solution for lifelong intelligent fault diagnosis in the health management of high-end equipment throughout its full lifecycle.