Majorities help minorities: Hierarchical structure guided transfer learning for few-shot fault recognition

Abstract To ensure the operational safety and reliability, fault recognition of complex systems is becoming an essential process in industrial systems. However, the existing recognition methods mainly focus on common faults with enough data, which ignore that many faults are lack of samples in engineering practice. Transfer learning can be helpful, but irrelevant knowledge transfer can cause performance degradation, especially in complex systems. To address the above problem, a hierarchy guided transfer learning framework (HGTL) is proposed in this paper for fault recognition with few-shot samples. Firstly, we fuse domain knowledge, label semantics and inter-class distance to calculate the affinity between categories, based on which a category hierarchical tree is constructed by hierarchical clustering. Then, guided by the hierarchical structure, the samples in most similar majority classes are selected from the source domain to pre-train the hierarchical feature learning network (HFN) and extract the transferable fault information. For the fault knowledge extracted from the child nodes of one parent node are similar and can be transferred with each other, so the trained HFN can extract better features of few samples classes with the help of the information from similar faults, and used to address few-shot fault recognition problems. Finally, a dataset of a nuclear power system with 65 categories and the widely used Tennessee Eastman dataset are analyzed respectively via the proposed method, as well as state-of-the-art recognition methods for comparison. The experimental results demonstrate the effectiveness and superiority of the proposed method in fault recognition with few-shot problem.