Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

Utilities that operate nuclear power plants are increasingly using probabilistic risk assessments (PRAs) to make day-to-day decisions on design, operations, and maintenance and to support risk-informed applications. These applications require high-quality and complete PRAs to ensure that the decisions and proposed changes are technically well-founded. Such PRAs include the modeling and quantification of PRA models for accident sequences initiated by internal floods and high-energy line breaks. To support PRA updates and upgrades for such sequences, the Electric Power Research Institute (EPRI) has sponsored ongoing research to develop and refine guidance and generic data that can be used to estimate initiating event frequencies for internal flood- and high-energy line break-induced accident sequences. In 2023, EPRI published the fifth revision of a generic database for these initiating event frequencies. This revision produced advancements in the methodology for passive component reliability, including the quantification of aging effects on pipe rupture frequencies and the capability to adjust these frequencies to account for enhancements to integrity management strategies associated with leak inspections and non-destructive examinations. The purpose of this paper is to present these enhancements and illustrate their application with selected examples.