A probabilistic-driven approach for early design quality risk and crux identification using non-Markovian stochastic Petri nets

Abstract Quality risk analysis of high-process-oriented systems, which refers to their ability to achieve required tasks on time, receives little attention during the early conceptual design stage, primarily due to the high level of abstraction when the system form is not yet fully defined. Although several mathematical methods exist to address this issue, they are fragmented across domains and lack a unified integration into early design practice. To address this problem, this paper introduces a novel approach that models design problems as discrete events with output conflict representation, using the non-Markovian stochastic Petri net. The framework is further integrated with mathematical techniques, including semi-Markov performance evaluation, sensitivity analysis, and uncertainty analysis, to quantify quality risks and identify the design crux (the most critical design parameters). By incorporating Monte Carlo simulations, it facilitates designers and engineers with early insights and allows them to compare alternative design specifications. Its applicability is demonstrated through a case study on the conceptual development of a remote maintenance system for the In-Bioshield area of the EU-DEMO fusion power plant. Initial results showed potential in identifying quality risks, addressing key factors contributing to the design problem, and finding optimal design specifications in the early stages.