Abstract
One can find many reliability, availability, and maintainability (RAM) models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD) of safety equipment: (1) by demand-caused and (2) standby-related failures. It proposes a maximum likelihood estimation (MLE) approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.
Highlights
The safety of nuclear power plants (NPPs) depends on the availability of safety-related components that are normally on standby and only operate in the case of a true demand
There is a need to fit the best model to real data by estimating the model parameters using an appropriate tool
Maximum likelihood estimation (MLE) using a direct search algorithm based on the Nelder-Mead Simplex (NMS) method is used to estimate maintenance effectiveness and ageing rate simultaneously
Summary
The safety of nuclear power plants (NPPs) depends on the availability of safety-related components that are normally on standby and only operate in the case of a true demand These components typically have two main types of failure modes that contribute to the probability of failure on demand:. (b) standby-related failure, associated with a standby hazard function (h) Both are generally associated with constant values in a standard Probabilistic Risk Assessment (PRA) models, that is, d0 and h0, respectively. A Bayesian approach is used to combine such generic probability density functions with plant specific failure data for each particular component [1,2,3,4] Both failure modes are often affected by degradation such as demand-related stress and ageing, which cause the component to degrade with chronological time and to fail. Different approaches have been proposed in the literature to model time-dependent d and h that take into account such effects in an either implicit or explicit way
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