Multi-State Reliability Analysis of Nuclear Power Plant Systems

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. The exponential distribution is characterized by memoryless property and hence is not able to distinguish between a newly installed system and a system that has already been in service. The primary rationale to move to semi-Markov process model is the ability to address this shortcoming in the Markov process model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of