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.

The Fukushima nuclear disaster has raised the importance on the reliability and risk research of the spent fuel pool (SFP), including the risk of internal events, fire, external hazards and so on. From a safety point of view, the low decay heat of the spent fuel assemblies and large water inventory in the SFP has made the accident progress goes very slow, but a large number of fuel assemblies are stored inside the spent fuel pool and without containment above the SFP building, it still has an unignored risk to the safety of the nuclear power plant. In this paper, a standardized approach for performing a holistic and comprehensive evaluation approach of the SFP risk based on the probabilistic safety analysis (PSA) method has been developed, including the Level 1 SFP PSA and Level 2 SFP PSA and external hazard PSA. The research scope of SFP PSA covers internal events, internal flooding, internal fires, external hazards and new risk source-fuel route risk is also included. The research will provide the risk insight of Spent Fuel Pool operation, and can help to make recommendation for the prevention and mitigation of SFP accidents which will be applicable for the SFP configuration risk management.

In a nuclear power plant, the consequences of a multi-unit event occurring concurrently are more serious than those of a single-unit event. The first step in the probabilistic safety analysis of multi-units is to analyze the initiating events and calculate the frequency of initiating events for simultaneous events of multiple units. The difficulty in using the fault tree model is that the known data are all frequency data from a single unit and cannot be logically multiplied. In this paper, taking a dual unit as an example, we used the formula to convert the probability of failure of the second unit within 72 h and then build a fault tree model. After analyzing the results of the dual unit, the most frequent cut set was the common cause of failure of the main transformer and of the switching failure of the main and auxiliary external power. The final calculation of the frequency of simultaneous loss of off-site power events for the dual units within 72 h was 3.22 × 10⁻⁴/year. After comparing with the single-unit results, it was found that the common cause failure of each unit’s independent equipment was the main reason for the occurrence of a loss of off-site power. Shared equipment in a single unit was ranked low in all the cut sets (such as the stability of the external grid for the main and auxiliary power systems) but was ranked high in multiple units. The calculation results of the frequency of initiating events of double units were two orders of magnitude lower than those of a single unit. However, the consequences of simultaneous events of multiple units were higher than those of single reactors. Therefore, attention should be paid to the risk of a simultaneous loss of off-site power event of multiple units.

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.

Traditional reliability allocation methods face three inherent limitations: (1) the insufficient consideration of influencing factors, (2) the equal treatment of impact factors, and (3) the presence of fuzzy information. To overcome these issues, we propose an integrated reliability allocation method that combines a two-layer integrated factors framework with a cloud model. First, a two-layer framework of influencing factors was constructed, and a mathematical model for global evaluation based on these factors was developed, along with an indicator allocation model derived from the evaluation model. The cloud model was employed to quantitatively express experts’ linguistic evaluations, eliminating the influence of randomness and fuzziness in the assessment process. Both theoretical analysis and practical examples show that the proposed method outperforms three other reliability allocation methods.