The Interpretable Reasoning and Intelligent Decision-Making Based on Event Knowledge Graph With LLMs in Fault Diagnosis Scenarios

With the emergence of large language models (LLMs), artificial intelligence (AI) has experienced revolutionary advancements. Fault diagnosis and maintenance, as crucial components of industrial production, can also undergo significant technological innovations. However, as black-box models, LLMs have three main drawbacks in fault diagnosis scenarios that require deep and responsible reasoning and decision-making: 1) LLMs face constraints in acquiring real-time factual updates knowledge within fault diagnosis scenarios and in training high-precision, domain-specific models through fine-tuning; 2) the inference results of LLMs lack interpretability and traceability when dealing with multilevel logical reasoning; and 3) in specific domains, LLMs often struggle with factual hallucinations. An effective method to address these issues is to integrate external knowledge sources into the reasoning processes of LLMs. Based on the complementarity between LLMs and knowledge graphs (KGs), this article proposes a new framework called think on fault diagnosis event KG (To-FD-EKG), which consists of two main components: a knowledge modeling module and a knowledge reasoning module: 1) the knowledge modeling module includes the proposed table-sequence encoder model for jointly extracting multiple relations and high-density event entities (TSM-JERHDE) from real industrial scenarios, constructing the fault diagnosis event KG (FD-EKG), and creating a virtual fault diagnosis digital twin environment (VFD-DTEnv). This provides reliable and updatable external knowledge for LLM reasoning, thereby extending its knowledge boundaries and 2) treating LLMs as agents, the knowledge reasoning module generates both thought paths and fault diagnosis actions by interactively retrieving and reasoning on the constructed VFD-DTEnv while retrospectively assessing the global context. The reasoning process is constrained by the FD-EKG, generating observable and traceable reasoning paths, reducing hallucinations, and enhancing the interpretability of the results. We aggregated two years of maintenance logs from an operational wind farm to construct a textual dataset for wind turbine (WT) fault diagnosis. By assessing 1227 WT maintenance logs, the outcomes of our practical case experiment underscore the effectiveness of the proposed methodology.