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首页> 外文期刊>Indian Journal of Science and Technology >Modeling and Analysis of Early-Warning Detection Module in Simulation Lifecycle Management Framework Using Copula Function
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Modeling and Analysis of Early-Warning Detection Module in Simulation Lifecycle Management Framework Using Copula Function

机译：仿真生命周期管理框架中使用Copula函数的预警检测模块建模与分析

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摘要

The simulation lifecycle management framework is considered as an advanced manufacturing information system integrating product lifecycle management and manufacturing execution systems. While other manufacturing systems focus on the detections of current faults and the related controls, the framework has an early-warning detection module for predicting potential risks and for preventing them in advance. In order to design the module, the preliminary procedure is to construct the mapping model between manufacturing data and quality-based indicators. The mapping model is indicated as a nonlinear meta model. While neural network based models or response surface methods are applied for the meta model, it is limited in the fact that it is difficult to capture correlations among atypical manufacturing big data. In order to overcome the issue, a copula based nonlinear meta model is suggested with a numerical case study for clear understandings. The usage of copula theories helps to extract well-defined relationships among manufacturing data. The potential risks are predicted using the copular-based meta model and advanced controls are taken for preventing them effectively.
机译：模拟生命周期管理框架被认为是将产品生命周期管理和制造执行系统集成在一起的高级制造信息系统。当其他制造系统专注于检测当前故障和相关控制时，该框架具有预警检测模块，用于预测潜在风险并提前预防风险。为了设计模块，初步程序是构建制造数据和基于质量的指标之间的映射模型。映射模型表示为非线性元模型。虽然将基于神经网络的模型或响应面方法应用于元模型，但由于难以捕获非典型制造大数据之间的相关性，因此存在局限性。为了克服这个问题，提出了一个基于copula的非线性元模型，并通过数值案例研究来获得清晰的理解。 copula理论的使用有助于在制造数据之间提取明确定义的关系。使用基于copular的元模型预测了潜在风险，并采取了先进的控制措施来有效地预防这些风险。

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