In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast amounts of potentially valuable data are being generated. Methods from Machine Learning (ML) and Data Mining (DM) have proven to be promising in extracting complex and hidden patterns from the data collected. The knowledge obtained can in turn be used to improve tasks like diagnostics or maintenance planning. However, such data-driven projects, usually performed with the Cross-Industry Standard Process for Data Mining (CRISP-DM), often fail due to the disproportionate amount of time needed for understanding and preparing the data. The application of domain-specific ontologies has demonstrated its advantageousness in a wide variety of Industry 4.0 application scenarios regarding the aforementioned challenges. However, workflows and artifacts from ontology design for CPPSs have not yet been systematically integrated into the CRISP-DM. Accordingly, this contribution intends to present an integrated approach so that data scientists are able to more quickly and reliably gain insights into the CPPS. The result is exemplarily applied to an anomaly detection use case.

翻译：在工业4.0与赛博物理生产系统（CPPSs）时代，大量具有潜在价值的数据正在生成。机器学习（ML）与数据挖掘（DM）方法已被证明能够有效提取数据中复杂且隐藏的模式。所获取的知识可进一步用于改进诊断或维护规划等任务。然而，此类通常采用跨行业数据挖掘标准流程（CRISP-DM）实施的数据驱动项目，常因数据理解与准备阶段耗时过长而失败。针对上述挑战，领域特定本体在多种工业4.0应用场景中已展现出显著优势。但面向CPPSs的本体设计工作流与工件尚未被系统性地集成至CRISP-DM中。为此，本文提出一种集成方法，使数据科学家能够更快速、更可靠地获取对CPPSs的洞见。该成果以异常检测用例进行了示范性应用。