Causality and Equipment Structure Enhanced Maintenance Plan Recommendation with Knowledge Graph Integration

Recommending maintenance plans presents significant challenges due to the low standardization of maintenance records and unclear pathways for identifying appropriate plans. While knowledge graphs have been extensively researched for integrating and evolving maintenance data, these issues hinder the accurate recommendation of maintenance solutions within large-scale maintenance knowledge systems. This paper proposes a causality and equipment structure enhanced maintenance plan matching and recommendation (CEE-MPMR) method to address these challenges. The method leverages an unsupervised SimCSE model to normalize domain vocabulary in the absence of domain lexicon, and proposes a maintenance plan reasoning method based on RotatE cc. The proposed method achieves a maintenance plan matching accuracy of 90.80%, effectively improving the precision of maintenance plan recommendations. Finally, we applied and validated the approach on real-world data from a nuclear power enterprise and integrated the algorithm into a maintenance plan recommendation system, supporting intelligent analysis and decision-making for nuclear complex equipment maintenance.