An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Fault diagnosis

Abstract

Various advanced reactor designs proposed in recent years envision deployment scenarios which feature reactor operations with significantly reduced staffing or even completely autonomous frameworks to reduce the operations and management costs of the plants. Many SMR and microreactor designs feature extended fuel cycles which limit inspection intervals, reduced access to critical components, and load-following capabilities that expose the reactor unit to different transients. Safe and reliable semi- or fully autonomous operations under these challenging operational regimes must be enabled through an on-line monitoring (OLM) system that effectively detects and diagnoses malfunctions in the reactor plant. This work presents the development of the Fault Diagnosis Module of the previously proposed data-driven OLM system for reactor operations: the Fault Detection and Diagnosis Monitoring System (FDDMS). The Fault Diagnosis Module monitors various sensor signatures from multiple systems and components at a nuclear power plant and accurately diagnoses the type and location of a malfunction. When integrated into the complete FDDMS methodology, the Fault Diagnosis Module can provide power transient dependent fault characterization by using separate convolutional neural network (CNN) models for steady state, ramping up in power, and ramping down in power operations, making the FDDMS especially applicable to load-following operational strategies. Two separate diagnosis approaches were explored in this paper for the Fault Diagnosis Module architecture: Hierarchical and End-to-End. The Hierarchical approach is a two-stage architecture in which the first stage uses a single CNN to classify the plant subsystem where the malfunction initiated. Subsequently in the second stage, a separate CNN is used for each subsystem to describe the specific fault type. The End-to-End approach uses a single CNN to directly classify the fault type from the overall list. To efficiently utilize computational resources, the hyperband intelligent hyperparameter optimization method to generate optimal CNN architectures. The diagnosis approaches were compared in terms of precision rate, recall rate, F1-score, and total accuracy for the possible fault types. Both diagnosis approaches produced good and satisfactory performance by generating total diagnosis accuracies above 99% on 17 different malfunction scenarios for all three power transient datasets. Additionally, robustness against noisy sensor data was tested, with models maintaining 99–100% accuracy at various levels of noise, and an illustrative real-time application of the methodology is provided.