Integrating Random Shocks Into Multi-State Physics Models of Degradation Processes for Component Reliability Assessment

International audienceWe extend a multi-state physics model (MSPM) framework for component reliability assessment by including semi-Markov and random shock processes. Two mutually ex-clusive types of random shocks are considered: extreme, and cumulative. Extreme shocks lead the component to immediate failure, whereas cumulative shocks simply affect the component degradation rates. General dependences between the degradation and the two types of random shocks are considered. A Monte Carlo simulation algorithm is implemented to compute component state probabilities. An illustrative example is presented, and a sensitivity analysis is conducted on the model parameters. The results show that our extended model is able to characterize the influences of different types of random shocks onto the component state probabilities and the reliability estimates

Fuzzy Reliability Assessment of Systems with Multiple Dependent Competing Degradation Processes

International audienceComponents are often subject to multiple competing degradation processes. For multi-component systems, the degradation dependency within one component or/and among components need to be considered. Physics-based models (PBMs) and multi-state models (MSMs) are often used for component degradation processes, particularly when statistical data are limited. In this paper, we treat dependencies between degradation processes within a piecewise-deterministic Markov process (PDMP) modeling framework. Epistemic (subjective) uncertainty can arise due to the incomplete or imprecise knowledge about the degradation processes and the governing parameters: to take into account this, we describe the parameters of the PDMP model as fuzzy numbers. Then, we extend the finite-volume (FV) method to quantify the (fuzzy) reliability of the system. The proposed method is tested on one subsystem of the residual heat removal system (RHRS) of a nuclear power plant, and a comparison is offered with a Monte Carlo (MC) simulation solution: the results show that our method can be most efficient

Petri-Net Simulation Model of a Nuclear Component Degradation Process

International audienceMulti physical state modeling (MPSM) is a novel approach being investigated for estimating the reliability of components and systems in the context of probabilistic risk assessment (PRA). The approach integrates multi-state modeling, which describes the degradation process by transitions among discrete states (e.g. initial, micro-crack, rupture, etc) and physical modeling by (physical) equations that govern the degradation process. In practice, the degradation process is non-Markovian and its transition rates are time-dependent and influenced by external factors such as temperature and stress. Under these conditions, it is in general difficult to derive the state probabilities analytically. On the contrary, Petri nets provide a flexible modeling framework for describing degradation processes with arbitrary transition rates. In this paper, we build a Petri net in support of Monte Carlo simulation of the stochastic aging behavior of a nuclear component undergoing stress corrosion cracking. The results are compared with analytical results derived in a previous work of literature