Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance

TL;DR

This paper presents a structured method combining ontology design with CRISP-DM to enhance data-driven maintenance strategies for Cyber-Physical Systems, demonstrated through an anomaly detection case study.

Contribution

It introduces a three-phase approach integrating domain expert knowledge with data mining processes for tailored CPS maintenance ontologies.

Findings

Effective ontology development for CPS maintenance

Improved anomaly detection accuracy

Semantic annotation enhances data interpretability

Abstract

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.