In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision-support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.

中文翻译：

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基于过程绩效模型的可重用知识库的分析和优化。

在本文中，我们提出了一种架构设计和软件框架，用于快速开发动态生产过程的描述性，诊断性，预测性和规范性分析解决方案。提议的体系结构和框架将支持过程性能模型的模块化，可扩展和可重用知识库（KB）的存储。该方法需要开发自动化方法，该方法可以将可重用KB中的高级模型转换为各种基础分析工具所需的低级专业模型，包括数据处理，优化，统计学习，估计和仿真。我们还为可​​重用的KB提出了一个组织和关键结构，由原子和复合过程性能模型以及特定于域的仪表板组成。此外，我们通过为流程工程师设计决策支持系统的原型来说明所建议的体系结构和框架的使用。决策支持系统允许用户通过图形用户界面分层组成和优化动态生产过程。