Global Sensitivity Analysis in a Multi-State Physics Model of Component Degradation Based on a Hybrid State-Space Enrichment and Polynomial Chaos Expansion Approach
This paper extends previous works related to the assessment of component degradation, through Markov multi-state physic models. The extension includes the evaluation of the effects of uncertain parameters in the model, and the definition of their importance with respect to their influence on the output of the model. Global Sensitivity Analysis (GSA) is selected as the technique because it enables us to 1) consider the simultaneous effects of parameters variations, and 2) to define importance indexes that allow a ranking of the components. GSA requires a large number of evaluations for specific points, identified by an appropriate design of experiment. To avoid the many costly evaluations, a meta-model is built based on polynomial chaos expansion (PCE). A PCE is a multi-dimensional polynomial approximation of the model with coefficients determined by evaluating the model in a reduced set of predetermined points. Importance index values are then derived directly from the PCE. Because, in the problem considered, the model provides the time-dependent behavior of the state probabilities, the importance indexes are also functions of time. An application is presented, related to the cracking process in an Alloy 82/182 dissimilar metal weld in the primary coolant system of a nuclear power plant.
global sensitivity analysis multi-state physics model degradation state-space enrichment polynomial chaos expansion inhomogeneous continuous time markov chain parameter uncertainty