A Knowledge Graph-based Link Prediction for Interpretable Maintenance Planning in Complex Equipment

Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. Many data points will be collected during the monitoring and maintenance of sophisticated equipment thanks to the large-scale sensor systems installed in contemporary factories. As a result, with the help of collected maintenance data, maintenance plans may be more detailed and timelier. A knowledge graph (KG) has recently been proposed to manage massive and unorganized maintenance data semantically, enhancing data usage. Despite the fact that previous research had utilized KG for maintenance planning, they had only used semantic searching or graph structure-based algorithms and had not included the prediction of new links. To fill this gap, a maintenance-oriented KG is established firstly based on the well-defined ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to find the potential solutions and explain the fault, specifically for the heterogeneous and sparse graph structure of maintenance-orient KG. A maintenance case of oil drilling equipment is carried out, which compares the proposed model with other cutting-edge models to demonstrate its effectiveness in link prediction.