A Cross-Phase Prognostic Framework for Emergent Work Risk Assessment and Schedule Decision Support in Nuclear Refueling Outages

Abstract

Refueling outages are among the most schedule-critical phases in a nuclear plant’s operating life. The premise of this paper is that outages are governed by two persistent challenges across both the planning and execution phases. The first is the inherent variability in maintenance activity completion times. Activity durations depend on component health state, crew availability, and field findings that deviate from the plan, yet current practice sizes contingency buffers through engineering judgment rather than data-driven uncertainty quantification. The second is unplanned activities emerging during outage execution, where a situation found during a maintenance, testing, or surveillance activity (with potential consequences on outage execution) requires to be addressed. This paper presents a cross-phase analytical framework that addresses both of these challenges using a single architecture built on historical condition report records, work order histories, and outage schedule data as primary inputs. In the planning phase, a proactive pipeline processes the multi-cycle condition report and work order history through a sequence of analytical stages that construct a component knowledge graph, assess component health-state trajectories by analyzing degradation trends across successive outage cycles, and compute a composite risk score combining degradation frequency, prior emergent work precedent, and component criticality. Components exhibiting escalating health deterioration are flagged even when no prior unplanned maintenance record exists, enabling the detection of first-failure components that would otherwise receive a zero risk score under conventional evidence-based methods. In the execution phase, a reactive pipeline accepts a condition report, and it produces a structured recommendation (proceed, defer, escalate, or monitor) supported by a duration estimate derived from the semantic retrieval of analogous historical work orders at the median and 80th-percentile levels via relevance-weighted empirical quantiles, alongside an analog count as a calibrated confidence signal.