Research Status and Application Prospects of Deep Learning in Fault Diagnosis for Nuclear Power Plants

Ensuring the high reliability of nuclear energy systems constitutes a strategic imperative for national energy security, with intelligent fault diagnosis serving as a cornerstone of their operational integrity. Conventional diagnostic approaches are inherently limited in their ability to effectively address the nonlinear, high-dimensional, and dynamically coupled nature of nuclear facilities. This limitation particularly pertains to the aspects of timeliness, accuracy, and generalization capability. Recent advancements in deep learning have exhibited transformative potential through end-to-end feature learning and spatiotemporal pattern recognition, achieving state-of-the-art performance in diagnosing faults in critical subsystems such as reactor coolant pumps, primary loops, and sensor arrays, with reported accuracies exceeding 97% and millisecond-level response times. Nevertheless, prevailing methods remain hindered by intrinsic deficiencies in model interpretability, cross-regime generalizability, computational efficiency, and heavy reliance on large-scale labeled data—barriers that impede their trustworthy deployment in safety-critical contexts. To address these challenges, future research must advance multimodal diagnostic frameworks that fuse multiphysics information, develop physics-informed architectures that embed domain knowledge to enhance explainability, establish edge-cloud collaborative inference mechanisms suited to resource-constrained operational environments, and realize self-evolving diagnostic models capable of continual learning from limited or imbalanced data. Collectively, these directions are pivotal to transitioning nuclear fault diagnosis from a paradigm of “high accuracy” toward one defined by high trustworthiness, robustness, and adaptive intelligence. This provides the theoretical grounding and technological pathway for the intelligent operation and maintenance of next-generation nuclear energy systems.