On a Uniform Causality Model for Industrial Automation

TL;DR

This paper introduces a Uniform Causality Model tailored for industrial automation, enabling better integration of causality concepts in complex Cyber-Physical Systems for improved diagnosis and machine learning applications.

Contribution

It proposes a new causality model designed specifically for industrial CPS, bridging the gap between data-driven causality inference and engineering system construction.

Findings

Model effectively describes CPS behavior mathematically

Applicable across various industrial automation domains

Supports integration with machine learning approaches

Abstract

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.