Monte Carlo simulation convergences’ percentage and position in future reliability evaluation

Abstract

<span lang="EN-US">Reliability assessment is a needed assessment in today's world. It is required not only for system design but also to ensure the power delivered reaches the consumer. It is usual for fault to occur, but it is best if the fault can be predicted and the way to overcome it can be prepared in advance. Monte Carlo simulation is a standard method of assessing reliability since it is a time-based evaluation that nearly represents the actual situation. However, sequential Monte Carlo (SMC) typically took long-time simulation. A convergence element can be implemented into the simulation to ensure that the time taken to compute the simulation can be reduced. The SMC can be done with and without convergence. SMC with convergence has high accuracy compared to the SMC without convergence, as it takes a long time and has a high possibility of not getting accurate output. In this research, the SMC is subjected to five different convergence items to determine which converge simulation is the fastest while providing better performance for reliability evaluation. There are two types of convergence positions, namely input convergence and output convergence. Overall, output convergence shows the best result compared to input convergence.</span>

Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach

“…3) Difficulties in Convergence: Convergence can be a challenge in Monte Carlo simulation, particularly for models with complex or high-dimensional parameter spaces. Assessing convergence and determining when to stop the simulation may require careful monitoring and diagnostics [41].…”