A dynamic process for evaluating the reliability of fossil power plant assets. Issue 12 (14th September 2020)

• Record Type: Journal Article
• Authors: Palakodeti, Satyanarayana R.; Raju, P. K.; Guo, Huairui
• Is Part Of: Engineering reports. Volume 2:Issue 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-14
• Subjects: Monte Carlo simulation -- power law -- reliability -- repairable systems -- Weibull analysis; Engineering -- Periodicals; Computer science -- Periodicals; 620.005
• DOI: 10.1002/eng2.12277
• Languages: English
• ISSNs: 2577-8196

Abstract

Abstract: Budgets and maintenance programs in fossil power plants are frequently set based on engineering judgment rather than a Probabilistic Risk Assessment (PRA). Fossil power plants seldom use PRA due to a lack of practical processes, especially when field reliability data are not readily available. In the absence of field data, using general industry data may not be right for conducting PRA for a given organization as the operation and maintenance conditions significantly vary between organizations. To have a successful PRA, an organization needs to use its own field data, and have an efficient process for its organization. Studies have suggested using field data provides a robust model and accurate results. Use of Reliability Block Diagrams for Reliability, Availability, and Maintainability analysis is not new. However, applications of these techniques are new to fossil power plants. In this paper, we propose a practical process by integrating several existing reliability techniques for fossil plants to apply PRA. The process was tested at many power plants, and the results aligned well with the actual values. The methodology outlined in this paper is a forward‐looking tool for managers to predict system reliability, and proactively develop maintenance plans and budgets. Abstract : A probabilistic reliability assessment process and methods were developed and presented for the complex systems of a fossil power plant. The methodology is explained with a case study for repairable equipment.