In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems. Central to these methodologies is the accurate prediction of remaining useful life (RUL), which serves as a pivotal cornerstone for effective maintenance and operational decision-making. While significant advancements in computer hardware and artificial intelligence (AI) algorithms have catalyzed substantial progress in AI-based RUL prediction, extant research frequently exhibits a narrow focus on specific algorithms, neglecting a comprehensive and comparative analysis of AI techniques across diverse equipment types and operational scenarios. This study endeavors to bridge this gap through the following contributions: (1) A rigorous analysis and systematic categorization of application scenarios for equipment RUL prediction, elucidating their distinct characteristics and requirements. (2) A comprehensive summary and comparative evaluation of several AI algorithms deemed suitable for RUL prediction, delineating their respective strengths and limitations. (3) An in-depth comparative analysis of the applicability of AI algorithms across varying application contexts, informed by a nuanced understanding of different application scenarios and AI algorithm research. (4) An insightful discussion on the current challenges confronting AI-based RUL prediction technology, coupled with a forward-looking examination of its future prospects. By furnishing a meticulous and holistic understanding of the traits of various AI algorithms and their contextual applicability, this study aspires to facilitate the attainment of optimal application outcomes in the realm of equipment RUL prediction.

This paper explores the application and evolution of Configuration Risk Management (CRM) systems in Chinese nuclear power plants, focusing on their alignment with the National Nuclear Safety Administration (NNSA)’s technical policies. It examines how CRM integrates risk management action matrices, risk limits, and monitoring tools to manage risks effectively across both second-generation plants with a higher baseline Core Damage Frequency (CDF) and third-generation plants with a lower baseline CDF. The study highlights the significant advancements in CRM system development, including the improvement of risk monitoring tools and the establishment of standardized technical guidelines, and underscores the critical role of ongoing regulatory support and CRM system enhancement to ensure the safe and sustainable operation of nuclear power plants in China, offering valuable insights for future nuclear safety management.

Lead-bismuth eutectic (LBE) is a eutectic alloy of lead (44.5 at%) and bismuth (55.5 at%) that can be used as the coolant for the fast nuclear reactors. In the event of specific conditions or even accidents of the reactors, the temperature of liquid LBE decreases, and it may undergo solidification and volume expansion during the aging process after solidification, which can easily cause damage to the reactor’s internal structure as well as the reactor vessels. In this study, the microstructure and mechanical properties of solidified LBE obtained at different cooling rates are systematically investigated after different aging times. It was found that the internal structure of LBE after aging remained a eutectic microstructure, consisting of the γ-phase (Bi-rich phase) and β-phase (Pb7Bi3). After a long period of static aging, the white γ-phase precipitated into the black β-phase, which further confirms the phase transition mechanism. Meanwhile, the acceleration of the cooling rate can aggravate volume expansion. As the aging time increases, there is no significant difference in the compressive yield strength σ of the LBE samples with the same cooling rate and only a certain degree of fluctuation. The elastic modulus E also shows similar results, indicating that aging time has a minor effect on the compressive yield strength σ and elastic modulus E of the LBE. With the increase in cooling rate, the compressive yield strength σ shows an upward trend, while the elastic modulus E is not significantly affected, with a small amplitude of fluctuation. Meanwhile, the hardness of LBE samples after long-term aging treatment is enhanced. After long-term aging, the overall density of the LBE samples shows a decreasing trend, the density fluctuation range of the fast cooling rates (5 K/min and 10 K/min) are significantly larger than that of the slow cooling rates. The decrease in density leads to volume expansion of the LBE during the aging process after solidification.

Traditional in-service inspection (ISI) methods for pipelines have certain limitations in identifying pipeline leakages. When these methods are directly applied to the ISI of Hua-long pressurized reactor (HPR1000) nuclear power plants, where the system complexity has significantly increased, they may lead to insufficient inspection efficiency and an extremely heavy workload. In this study, based on the framework of typical risk-informed analysis methods for nuclear power plants in the industry and integrating domestic engineering practical experience, an optimized ISI model for pipelines in HPR1000 nuclear power plants was constructed, and a pilot application was carried out on the chemical and volume control system (RCV) of the primary circuit. The inspection strategy was optimized through a series of steps, including determining the analysis scope, conducting pipe segment failure analysis, constructing a risk matrix, selecting inspection elements, and assessing risk impacts. Case studies showed that the risk-informed in-service inspection (RI-ISI) method successfully classified over 3000 welds in the RCV system based on risk levels (high, medium, low). After optimization, 16 low-risk welds (risk level 7) and one of the two medium-risk welds (risk level 4) that originally required volumetric inspection were exempted from inspection. Quantitative risk analysis confirmed that the increments in core damage frequency (CDF) and large early-release frequency (LERF) caused by this optimization were far below the regulatory limits. This method significantly reduces the inspection burden of medium- and low-risk pipelines while ensuring that high-risk areas receive priority attention, providing important technical support for the safe and efficient operation and maintenance of HPR1000 and subsequent third-generation nuclear power units.