Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs
Milapji Singh Gill, Tom Jeleniewski, Felix Gehlhoff, Alexander Fay
TL;DR
The paper tackles the problem of representing time-continuous CPS dynamics in knowledge graphs by contextualizing differential equations within lifecycle data. It introduces CPSMod, a modular, standards-aligned semantic model that uses OpenMath to encode differential operators for $ODE$ and $PDE$ representations, and an OM2RDF recursive mapping to instantiate these equations in RDF. The approach is validated in aviation maintenance on an Electro-Hydraulic Servo Actuator (EHSA), demonstrating formal representation of dynamics and linkage to functional and structural CPS data across lifecycle phases. By enabling interoperable, reusable CPS behavior representations and paving the way for digital twins and data-driven diagnostics, the work enhances how continuous dynamics can be integrated with traditional KG-based CPS knowledge.
Abstract
Time-continuous dynamic models are essential for various Cyber-Physical System (CPS) applications. To ensure effective usability in different lifecycle phases, such behavioral information in the form of differential equations must be contextualized and integrated with further CPS information. While knowledge graphs provide a formal description and structuring mechanism for this task, there is a lack of reusable ontological artifacts and methods to reduce manual instantiation effort. Hence, this contribution introduces two artifacts: Firstly, a modular semantic model based on standards is introduced to represent differential equations directly within knowledge graphs and to enrich them semantically. Secondly, a method for efficient knowledge graph generation is presented. A validation of these artifacts was conducted in the domain of aviation maintenance. Results show that differential equations of a complex Electro-Hydraulic Servoactuator can be formally represented in a knowledge graph and be contextualized with other lifecycle data, proving the artifacts' practical applicability.