RDF-based knowledge graph integration with deep learning for fault diagnosis

Combining system knowledge with deep learning for fault diagnosis in industrial applications offers the potential to reduce the dependency of deep learning algorithms on extensive labeled datasets. However, existing methods often rely on highly specialized, problem-specific knowledge or demand detailed physical insights into the system, which limits their generalizability. Additionally, inconsistencies in knowledge representation hinder the ability to compare and build upon prior approaches. In this work, we address these challenges by leveraging commonly available knowledge about the phase structure of systems and the hierarchical organization of condition spaces. This information is systematically represented using knowledge graphs (KGs) based on the Resource Description Framework (RDF). To integrate this knowledge into deep learning, we transform the input data and the corresponding labels based on the KGs, and employ a graph neural network (GNN) trained with a semantic loss function informed by the knowledge about the condition space. The proposed approach is evaluated on three diverse datasets with varying characteristics under the two scenarios of domain generalization and novel fault detection.