A Knowledge-Graph-Guided Diagnostic Pipeline for Root Cause Analysis of Equipment Failures in Nuclear Power Plant

Abstract

The root cause analysis of complex equipment failures in nuclear power plants requires the integration of heterogeneous data sources into traceable causal explanations. It is a process that can be time-consuming, expert-dependent, and difficult to audit across plant lifetime. This paper presents a structured diagnostic reasoning framework designed to copilot system engineers to perform root cause analyses, improving analysis speed, consistency, and traceability while preserving full engineering interpretability. We have formally decomposed a causal reasoning architecture that integrates five independent but interacting reasoning dimensions (i.e., structural, temporal, evidence-based, governance, and historical) allowing each dimension of the diagnostic process to be explicitly validated. The framework integrates several heterogeneous inputs such as telemetry signals, plant documentation, system architecture models, and operational context within a unified knowledge representation. Structural reasoning constrains the causal search space by identifying physically plausible failure modes based on system connectivity and component dependencies. Temporal reasoning characterizes the relationships between observed anomalies and the main event using interval-based representations to distinguish candidate causes from coincident or downstream effects. Evidence-based reasoning retrieves and evaluates relevant historical records, classifying them as supporting, contradicting, or contextual with respect to each candidate hypothesis. Candidate causes are generated and evaluated across multiple dimensions, including structural plausibility, temporal consistency, telemetry alignment, and documentary evidence strength, with explicit tracking of uncertainty and conflicting evidence. A synthesis stage produces an assessment that includes ranked hypotheses, supporting evidence with traceable references, identified uncertainties, and recommended follow-up actions. A key design principle of the framework is that causal conclusions are only promoted when multiple independent sources of evidence align, reducing the risk of premature convergence on incomplete or biased explanations. The framework is evaluated through mechanismlevel validation across three representative nuclear plant failure scenarios covering condenser degradation, reactor trip temporal gating, and surveillance check valve leakthrough.