Development of web-based reliability data analysis algorithm model and its application
- Authors
- Hwang, Seok-Won; Oh, Ji-Yong; Moosung Jae
- Issue Date
- Feb-2010
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Citation
- ANNALS OF NUCLEAR ENERGY, v.37, no.2, pp.248 - 255
- Indexed
- SCIE
SCOPUS
- Journal Title
- ANNALS OF NUCLEAR ENERGY
- Volume
- 37
- Number
- 2
- Start Page
- 248
- End Page
- 255
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/175509
- DOI
- 10.1016/j.anucene.2009.10.015
- ISSN
- 0306-4549
- Abstract
- For this study, a database model of plant reliability was developed for the effective acquisition and management of plant-specific data that can be used in various applications of plant programs as well as in Probabilistic Safety Assessment (PSA). Through the development of a web-based reliability data analysis algorithm, this approach systematically gathers specific plant data such as component failure history, maintenance history, and shift diary. First. for the application of the developed algorithm, this study reestablished the raw data types, data deposition procedures and features of the Enterprise Resource Planning (ERP) system process. The component codes and system codes were standardized to make statistical analysis between different types of plants possible. This standardization contributes to the establishment of a flexible database model that allows the customization of reliability data for the various applications depending on component types and systems. In addition, this approach makes it possible for users to perform trend analyses and data comparisons for the significant plant components and systems. The validation of the algorithm is performed through a comparison of the importance measure value (Fussel-Vesely) of the mathematical calculation and that of the algorithm application. The development of a reliability database algorithm is one of the best approaches for providing systemic management of plant-specific reliability data with transparency and continuity. This proposed algorithm reinforces the relationships between raw data and application results so that it can provide a comprehensive database that offers everything from basic plant-related data to final customized data.
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