Monte Carlo simulation-based defect ratio estimation approach for a chemical materials stockpile reliability program

Abstract

A chemical material stockpile reliability program (CSRP) that determines the usability, safety, reliability, and performance of chemical equipment and materials is developed to determine the storage or disposal of chemical material stockpile (Storage Chemical Equipment and Material Reliability Evaluation Instruction, 2019). However, current inspection for current CSRP depend on test and evaluation of criteria for level of importance, and so the number of samples and acceptance quality limit (AQL) are presented based on the lot size. All the processes are conducted under KS Q ISO 2859-1, and the defect rate of the entire lot of CSRP items is generally assumed to be a distribution that is similar to a binomial distribution. However, the pass-fail test for CSRP items is based on approximately 10 test items, and the factors that cause defects in these items are also heterogeneous. We propose a new methodology for estimating the defect rates of CSRP items based on Monte Carlo simulations, which are widely used in various academic fields. In addition, we show the future applicability of the methodology by applying it to the K1 gas mask case and revealing the results of the defect rate estimation. We also present future work, including the need for a standard sample of CSRP items.