Stochastic Modeling of Deterioration in Nuclear Power Plant Components

Reliability;Deterioration;Nuclear Power Plants;Gamma Process;Stochastic Modeling;Maintenance Optimization;Civil Engineering

Yuan, Xianxun

University of Waterloo

关键词: Reliability;    Deterioration;    Nuclear Power Plants;    Gamma Process;    Stochastic Modeling;    Maintenance Optimization;    Civil Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/2756/1/thesis.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

The risk-based life-cycle management of engineering systems in a nuclear powerplant is intended to ensure safe and economically efficient operation ofenergy generation infrastructure over its entire service life. An importantelement of life-cycle management is to understand, model and forecast theeffect of various degradation mechanisms affecting the performance ofengineering systems, structures and components.The modeling of degradation in nuclear plant components is confounded by largesampling and temporal uncertainties. The reason is that nuclear systems arenot readily accessible for inspections due to high level of radiation andlarge costs associated with remote data collection methods. The models ofdegradation used by industry are largely derived from ordinary linearregression methods.The main objective of this thesis is to develop more advanced techniques basedon stochastic process theory to model deterioration in engineering componentswith the purpose of providing more scientific basis to life-cycle managementof aging nuclear power plants. This thesis proposes a stochastic gamma process(GP) model for deterioration and develops a suite of statistical techniquesfor calibrating the model parameters. The gamma process is a versatile andmathematically tractable stochastic model for a wide variety of degradationphenomena, and another desirable property is its nonnegative, monotonicallyincreasing sample paths. In the thesis, the GP model is extended by includingadditional covariates and also modeling for random effects. The optimizationof age-based replacement and condition-based maintenance strategies is also presented.The thesis also investigates improved regression techniques for modelingdeterioration. A linear mixed-effects (LME) regression model is presented toresolve an inconsistency of the traditional regression models. The proposedLME model assumes that the randomness in deterioration is decomposed into twoparts: the unobserved heterogeneity of individual units and additivemeasurement errors.Another common way to model deterioration in civil engineering is to treat therate of deterioration as a random variable. In the context of condition-basedmaintenance, the thesis shows that the random variable rate (RV) model isinadequate to incorporate temporal variability, because the deteriorationalong a specific sample path becomes deterministic. This distinction betweenthe RV and GP models has profound implications to the optimization ofmaintenance strategies.The thesis presents detailed practical applications of the proposed models tofeeder pipe systems and fuel channels in CANDU nuclear reactors.In summary, a careful consideration of the nature of uncertainties associatedwith deterioration is important for credible life-cycle management ofengineering systems. If the deterioration process is affected by temporaluncertainty, it is important to model it as a stochastic process.