Temporal knowledge graph fusion with neural ordinary differential equations for the predictive maintenance of electromechanical equipment

Abstract

Predictive Maintenance is the primary strategy for optimizing operational efficiency and reducing the maintenance costs of electromechanical equipment. However, existing Predictive Maintenance approaches suffer from significant shortcomings, such as the inability to learn the dynamic evolution of fault and maintenance events within massive, heterogeneous datasets and the lack of effective models to handle this complex data. To address these issues, we propose a temporal knowledge graph (TKG) reasoning method. First, we construct a TKG based on an ontology defined by the heterogeneous data features of electromechanical equipment. Second, we propose a Dynamic Graph Embedding model, which captures the dynamic evolution of the non-equal-interval events in the TKG by combining neural ordinary differential equations with a graph convolutional neural network. Furthermore, we design a Dynamic Hawkes Transformer to identify the evolutionary process and predicting future events based on historical fault and maintenance data. Finally, we use elevators as a case study to compare the proposed method with other advanced methods and demonstrate its effectiveness in TKG reasoning. Our proposed method excels in fault and maintenance event prediction, as well as time prediction, for electromechanical equipment.