{"title":"基于可编辑 GSPN 和自适应蒙特卡罗模拟的复杂系统可靠性评估方法","authors":"Yining Zeng, Youchao Sun, Tao Xu, Siyu Su","doi":"10.1002/sys.21736","DOIUrl":null,"url":null,"abstract":"This paper presents an innovative EGSPN (Editable Generalized Stochastic Petri Net) model, optimizing the size of GSPN (Generalized Stochastic Petri Net) while preserving its core benefits. Specifically, the EGSPN introduces a transition termed “editable transition,” which allows for the establishment of any dynamic logic, thereby circumventing the intricacies associated with the EGSPN model structure. A Monte Carlo (MC) simulation technique is put forth for EGSPN analysis, enhanced by the integration of Sobol sequences with a Bayesian optimizer. This MC simulation approach, unbounded by any specific distribution constraints, exhibits accelerated convergence rates. To address the issue of preset sampling size, an adaptive sampling strategy rooted in confidence intervals has been proposed. By real‐time computation of margin of errors under varying confidence levels, simulations can be halted prior to reaching the preset sampling size. This method strikes a balance between reduced simulation time and maintained accuracy. The applicability and efficacy of the proposed method are further elucidated through a numerical example of a flight control system.","PeriodicalId":509213,"journal":{"name":"Systems Engineering","volume":"14 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A reliability evaluation method for complex systems based on the editable GSPN and adaptive Monte Carlo simulation\",\"authors\":\"Yining Zeng, Youchao Sun, Tao Xu, Siyu Su\",\"doi\":\"10.1002/sys.21736\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an innovative EGSPN (Editable Generalized Stochastic Petri Net) model, optimizing the size of GSPN (Generalized Stochastic Petri Net) while preserving its core benefits. Specifically, the EGSPN introduces a transition termed “editable transition,” which allows for the establishment of any dynamic logic, thereby circumventing the intricacies associated with the EGSPN model structure. A Monte Carlo (MC) simulation technique is put forth for EGSPN analysis, enhanced by the integration of Sobol sequences with a Bayesian optimizer. This MC simulation approach, unbounded by any specific distribution constraints, exhibits accelerated convergence rates. To address the issue of preset sampling size, an adaptive sampling strategy rooted in confidence intervals has been proposed. By real‐time computation of margin of errors under varying confidence levels, simulations can be halted prior to reaching the preset sampling size. This method strikes a balance between reduced simulation time and maintained accuracy. The applicability and efficacy of the proposed method are further elucidated through a numerical example of a flight control system.\",\"PeriodicalId\":509213,\"journal\":{\"name\":\"Systems Engineering\",\"volume\":\"14 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/sys.21736\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/sys.21736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文提出了一种创新的 EGSPN(可编辑广义随机 Petri 网)模型,在保留 GSPN(广义随机 Petri 网)核心优势的同时,优化了其规模。具体来说,EGSPN 引入了一种称为 "可编辑过渡 "的过渡,允许建立任何动态逻辑,从而避免了与 EGSPN 模型结构相关的复杂性。通过将 Sobol 序列与贝叶斯优化器相结合,为 EGSPN 分析提出了蒙特卡罗(MC)模拟技术。这种 MC 仿真方法不受任何特定分布约束的限制,具有更快的收敛速度。为了解决预设采样大小的问题,我们提出了一种植根于置信区间的自适应采样策略。通过实时计算不同置信度下的误差幅度,可以在达到预设抽样规模之前停止模拟。这种方法在缩短模拟时间和保持精度之间取得了平衡。通过一个飞行控制系统的数值示例,进一步阐明了所提方法的适用性和有效性。
A reliability evaluation method for complex systems based on the editable GSPN and adaptive Monte Carlo simulation
This paper presents an innovative EGSPN (Editable Generalized Stochastic Petri Net) model, optimizing the size of GSPN (Generalized Stochastic Petri Net) while preserving its core benefits. Specifically, the EGSPN introduces a transition termed “editable transition,” which allows for the establishment of any dynamic logic, thereby circumventing the intricacies associated with the EGSPN model structure. A Monte Carlo (MC) simulation technique is put forth for EGSPN analysis, enhanced by the integration of Sobol sequences with a Bayesian optimizer. This MC simulation approach, unbounded by any specific distribution constraints, exhibits accelerated convergence rates. To address the issue of preset sampling size, an adaptive sampling strategy rooted in confidence intervals has been proposed. By real‐time computation of margin of errors under varying confidence levels, simulations can be halted prior to reaching the preset sampling size. This method strikes a balance between reduced simulation time and maintained accuracy. The applicability and efficacy of the proposed method are further elucidated through a numerical example of a flight control system.