Distribution Agnostic Few Shot Industrial Fault Diagnosis via Adaptation Aware Optimal Feature TransDistribution Agnostic Few Shot Industrial Fault Diagnosis via Adaptation Aware Optimal Feature Trans

In complex real-world industrial systems, few-shot fault diagnosis greatly challenges model-free methods. Interest in domain adaptation methods, which enriches the diversity of accessible samples by narrowing the distance between the source and target domain distributions, has grown. However, these approaches generally rely on specific domain pairs and numerous labeled source data, which are difficult conditions to satisfy in industrial scenarios with complex/changeable working conditions and limited fault samples. Herein, we creatively propose a distribution-agnostic few-shot framework by imitating brain awareness process in unseen tasks. Our framework can generate a learnable and interpretable paradigm to learn common similarities in task embedding space, alleviating the dependence on deep supervised training and reducing the time required for conducting credible exploration from scratch. In particular, we design an adaptation-aware nonconvex matrix optimization procedure for optimal deep adaptation features transport process updating. Experimental results validate the superiority of our framework in two industrial applications, magnetic flux leakage, and bearing datasets, showing it is feasible and promising.