Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.

翻译：工业流程产生海量时间序列数据，然而从中提取有意义的关系与洞见仍具挑战性。本文提出一种从时间序列数据中自动化学习知识图谱的框架，专为工业应用场景设计。该框架针对工业数据集固有的复杂性，将其转化为可提升决策制定、流程优化与知识发现的知识图谱。此外，框架采用格兰杰因果关系识别关键属性，为预测模型的设计提供依据。为说明本方法的实际效用，我们同时展示了一个具有启发性的应用案例，证明该框架在真实工业场景中的优势。进一步地，我们演示了时间序列数据向知识图谱的自动化转换如何识别关键工艺参数间的因果影响或依赖关系。