Abstract: Named Entity Recognition plays a key role in identifying industrial equipment faults, aiding in fault prediction, maintenance management, and intelligent decision-making. Aiming at the nested structures and long spans in industrial equipment fault, this paper proposes a boundary-aware entity recognition method. This method accurately located the span of entities through boundary detection and enhanced recognition performance by combining category prediction to determine the entity span's category. To tackle the scarcity of labeled data, this paper constructed an entity recognition dataset targeted at industrial equipment faults. Experimental results demonstrate the effectiveness of this method in recognizing entities related to industrial equipment faults, which lays a solid foundation for subsequent data analysis and knowledge graph construction.