2 research outputs found
On a Uniform Causality Model for Industrial Automation
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
Diagnosis driven Anomaly Detection for CPS
In Cyber-Physical Systems (CPS) research, anomaly detection (detecting
abnormal behavior) and diagnosis (identifying the underlying root cause) are
often treated as distinct, isolated tasks. However, diagnosis algorithms
require symptoms, i.e. temporally and spatially isolated anomalies, as input.
Thus, anomaly detection and diagnosis must be developed together to provide a
holistic solution for diagnosis in CPS. We therefore propose a method for
utilizing deep learning-based anomaly detection to generate inputs for
Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and
a real-world CPS dataset, where our model demonstrates strong performance
relative to other state-of-the-art models