Fault Cause Identification across Manufacturing Lines through Ontology-Guided and Process-Aware FMEA Graph Learning with LLMs

Fault cause identification in complex engineered systems remains challenging due to system complexity, frequent reconfigurations, and the limited reusability of accumulated diagnostic knowledge, with automated manufacturing lines representing a prominent application domain. Although Failure Mode and Effects Analysis (FMEA) worksheets contain valuable expert insights, their reuse across heterogeneous system configurations is hindered by natural language variability, inconsistent terminology, and process differences. To address these limitations, we propose OGPAL (Ontology-Guided and Process-Aware Learning), a framework that enhances FMEA reusability by combining manufacturing-domain conceptualization with graph neural network reasoning. First, FMEA worksheets from multiple manufacturing lines are transformed into a unified knowledge graph through ontology-guided information extraction supported by a large language model (LLM), capturing domain concepts such as actions, states, components, and parameters. Second, a Relational Graph Convolutional Network (RGCN) with the process-aware scoring function learns embeddings that respect both semantic relationships and sequential process flows. Finally, link prediction is employed to retrieve and rank candidate fault causes consistent with the target line's process flow. A case study on automotive pressure sensor assembly lines demonstrates that OGPAL outperforms a state-of-the-art retrieval-augmented generation baseline (nDCG@20 = 0.450) and an RGCN approach (0.559), achieving the best performance (0.719) in fault cause identification. Ablation studies confirm the contributions of both LLM-driven domain conceptualization and process-aware learning. These results indicate that the framework effectively supports reasoning over heterogeneous diagnostic knowledge and improves the transferability of FMEA knowledge across manufacturing lines.