The risk-based life-cycle management of engineering systems in a nuclear power
plant is intended to ensure safe and economically efficient operation of
energy generation infrastructure over its entire service life. An important
element of life-cycle management is to understand, model and forecast the
effect of various degradation mechanisms affecting the performance of
engineering systems, structures and components.
The modeling of degradation in nuclear plant components is confounded by large
sampling and temporal uncertainties. The reason is that nuclear systems are
not readily accessible for inspections due to high level of radiation and
large costs associated with remote data collection methods. The models of
degradation used by industry are largely derived from ordinary linear
regression methods.
The main objective of this thesis is to develop more advanced techniques based
on stochastic process theory to model deterioration in engineering components
with the purpose of providing more scientific basis to life-cycle management
of aging nuclear power plants. This thesis proposes a stochastic gamma process
(GP) model for deterioration and develops a suite of statistical techniques
for calibrating the model parameters. The gamma process is a versatile and
mathematically tractable stochastic model for a wide variety of degradation
phenomena, and another desirable property is its nonnegative, monotonically
increasing sample paths. In the thesis, the GP model is extended by including
additional covariates and also modeling for random effects. The optimization
of age-based replacement and condition-based maintenance strategies is also presented.
The thesis also investigates improved regression techniques for modeling
deterioration. A linear mixed-effects (LME) regression model is presented to
resolve an inconsistency of the traditional regression models. The proposed
LME model assumes that the randomness in deterioration is decomposed into two
parts: the unobserved heterogeneity of individual units and additive
measurement errors.
Another common way to model deterioration in civil engineering is to treat the
rate of deterioration as a random variable. In the context of condition-based
maintenance, the thesis shows that the random variable rate (RV) model is
inadequate to incorporate temporal variability, because the deterioration
along a specific sample path becomes deterministic. This distinction between
the RV and GP models has profound implications to the optimization of
maintenance strategies.
The thesis presents detailed practical applications of the proposed models to
feeder pipe systems and fuel channels in CANDU nuclear reactors.
In summary, a careful consideration of the nature of uncertainties associated
with deterioration is important for credible life-cycle management of
engineering systems. If the deterioration process is affected by temporal
uncertainty, it is important to model it as a stochastic process.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/2756 |
| Date | January 2007 |
| Creators | Yuan, Xianxun |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | 1404842 bytes, application/pdf |
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