Zero-shot fault diagnosis of high-voltage circuit breakers: fusion of phase space reconstruction and attribute embedding methods

Traditional mechanical fault diagnosis methods for high-voltage circuit breakers (CBs) largely rely on data-driven learning from a substantial amount of labeled fault samples. However, the scarcity of target fault samples in practical engineering applications often limits diagnostic performance, leading to high misdiagnosis rates and poor generalization capabilities. To address these challenges, this study proposes an attribute embedding zero-shot diagnosis (AEZSD) method, designed to overcome the limitations of sample insufficiency. Initially, this paper utilizes phase space reconstruction techniques to thoroughly explore the intrinsic dynamic features of vibrational signals within CBs. Subsequently, by integrating the electromechanical signal characteristics of the CBs, the concept of fault attributes is introduced, and an attribute embedding learning network is constructed. Through this network and statistical rules, the proposed method can effectively identify previously unseen fault types. Experimental results confirm that the AEZSD method can leverage historical fault data to pre-learn fault attribute knowledge and accurately diagnose faults without target fault samples, providing a novel solution for CB fault diagnosis.