Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights and benefits. This paper introduces a data-driven approach for evaluating and improving the performance of production lines by monitoring the performance of both individual machines and their interactions as a system. The approach begins with a rigorous methodology for classifying machine states recorded by the Manufacturing Execution System (MES) into finer-grained substates, enabling a comprehensive analysis of machine cycle time variability. Subsequently, these substates are leveraged as a foundation for constructing performance monitoring models at both the machine and system levels, employing probabilistic automata for the machine level and logistic regression for the system level. The system-level performance monitoring model is constructed to predict a Flow metric that enables the prediction of abnormal behaviors and deviations from production targets. This data-driven approach serves as a foundational ingredient of a system-level digital twin, designed to provide production lines with insights that enable proactive implementation of measures aimed at optimizing overall manufacturing efficiency. Through an industrial test case from the automotive industry, the results demonstrate the capability of performance monitoring, capturing errors within confidence intervals, and establishing predictive cause-and-effect relationships between machines within the production system.

中文翻译：

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一种数据驱动的方法，用于生产线的机器和系统级性能监控数字孪生

生产系统中的高效性能监控至关重要，因为它使组织能够优化其制造流程、提高生产率并保持市场竞争优势。通常，机器和系统级性能监控系统是独立研究的，而考虑这两个级别的集成方法可以提供有价值的见解和好处。本文介绍了一种数据驱动的方法，通过监控单个机器的性能及其作为一个系统的交互来评估和提高生产线的性能。该方法首先采用严格的方法，将制造执行系统 ​​(MES) 记录的机器状态分类为更细粒度的子状态，从而能够对机器周期时间变化进行全面分析。随后，这些子状态被用作在机器和系统级别构建性能监控模型的基础，在机器级别采用概率自动机，在系统级别采用逻辑回归。系统级性能监控模型的构建是为了预测 Flow 指标，从而能够预测异常行为和与生产目标的偏差。这种数据驱动的方法是系统级数字孪生的基本组成部分，旨在为生产线提供洞察，从而能够主动实施旨在优化整体制造效率的措施。通过汽车行业的工业测试案例，结果证明了性能监控、捕获置信区间内的错误以及在生产系统内的机器之间建立预测因果关系的能力。