Advancing multimodal diagnostics: Integrating industrial textual data and domain knowledge with large language models

The rapid advancement and application of large language models (LLMs) in various domains prompt an investigation into their potential in the field of prognostics and health management (PHM), particularly for enhancing data-driven model capabilities. This study explores the integration of domain knowledge accumulated in unstructured text data such as technical documents and maintenance logs into diagnostics models using LLMs. The study demonstrates the new possibilities to exploit data that are traditionally underutilized due to their complexity and the presence of domain-specific jargon. By leveraging LLMs for contextual understanding and information extraction from such texts, this study proposes a novel approach that combines textual data with existing condition monitoring systems to improve the accuracy of diagnostics models. A case study on hydrogenerators illustrates the feasibility and value of integrating LLMs into PHM systems. The findings suggest that the incorporation of LLMs can lead to more informed, accurate diagnostics, ultimately enhancing operational efficiency and safety within industrial environments.