Knowledge Graph Construction Technology for Testing Faults of New Energy Permanent Magnet Synchronous Motors

The reliability and safety of permanent magnet synchronous motors (PMSMs), as core components of new energy vehicle drive systems, are critical for operational performance. Traditional fault diagnosis methods relying on expert experience face challenges in addressing complex fault patterns and conducting correlation analysis in motor systems. This paper proposes a knowledge graph construction technology specifically designed for PMSM testing fault diagnosis. Firstly, we systematically analyze typical PMSM structures, test types, fault patterns, characteristic signals, and causal relationships to establish a structured fault knowledge graph data model. The implementation leverages Neo4j graph database for instantiated storage and visualization. Secondly, we develop a fault reasoning methodology incorporating graph inference rules, which integrates test data characteristics to achieve precise fault localization and root cause tracing. Experimental results indicate that the proposed approach demonstrates superior performance in fault recognition accuracy, inference efficiency, and system interpretability. This research provides an effective technical framework for enhancing intelligent testing capabilities in electric motor quality assurance. The proposed knowledge graph architecture has been successfully implemented in automotive motor test platforms, showing promising application potential for predictive maintenance.