TY - GEN
T1 - Risk Models Utilizing Historical Plant Data for Optimizing Maintenance Strategy at Nuclear Power Plants
AU - Yadav, Vaibhav
AU - Gribok, Andrei V.
AU - Manjunatha, Koushik Araseethota
AU - Agarwal, Vivek
N1 - Publisher Copyright:
© 2023 American Nuclear Society, Incorporated.
PY - 2023
Y1 - 2023
N2 - One of the major contributors to the total operating costs today is the operations and maintenance costs which include a labor intense preventive maintenance program which involves manually performed inspection, calibration, testing, and maintenance of plant assets at a periodic frequency and the time-based replacement of assets, irrespective of its condition. This has resulted in a labor-centric business model to achieve high-capacity factors. It is time to transition from this labor-centric business model to achieve an optimal maintenance program. To enable this transition, a reliable method is needed based on available advanced technologies to support assessing the condition and risk of equipment failures. This paper presents a methodology for estimating and updating component risk parameters by utilizing historical maintenance data at nuclear power plants. Markov chain-based models are developed to represent healthy, degraded, and failed state of systems, structures, and components. The transition rates of Markov models are estimated using Bayesian estimation that utilizes historical maintenance data of components at a commercial nuclear power plant.
AB - One of the major contributors to the total operating costs today is the operations and maintenance costs which include a labor intense preventive maintenance program which involves manually performed inspection, calibration, testing, and maintenance of plant assets at a periodic frequency and the time-based replacement of assets, irrespective of its condition. This has resulted in a labor-centric business model to achieve high-capacity factors. It is time to transition from this labor-centric business model to achieve an optimal maintenance program. To enable this transition, a reliable method is needed based on available advanced technologies to support assessing the condition and risk of equipment failures. This paper presents a methodology for estimating and updating component risk parameters by utilizing historical maintenance data at nuclear power plants. Markov chain-based models are developed to represent healthy, degraded, and failed state of systems, structures, and components. The transition rates of Markov models are estimated using Bayesian estimation that utilizes historical maintenance data of components at a commercial nuclear power plant.
KW - Bayesian
KW - nuclear
KW - risk-informed maintenance
UR - https://www.scopus.com/pages/publications/85183322948
UR - https://www.mendeley.com/catalogue/9e86ff92-610d-3336-b03a-9e43b362c8f8/
U2 - 10.13182/NPICHMIT23-41207
DO - 10.13182/NPICHMIT23-41207
M3 - Conference contribution
AN - SCOPUS:85183322948
SN - 9780894487910
T3 - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
SP - 284
EP - 291
BT - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
PB - American Nuclear Society
T2 - 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
Y2 - 15 July 2023 through 20 July 2023
ER -