8 research outputs found
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study
Deep learning (DL) models have seen increased attention for time series
forecasting, yet the application on cyber-physical systems (CPS) is hindered by
the lacking robustness of these methods. Thus, this study evaluates the
robustness and generalization performance of DL architectures on multivariate
time series data from CPS. Our investigation focuses on the models' ability to
handle a range of perturbations, such as sensor faults and noise, and assesses
their impact on overall performance. Furthermore, we test the generalization
and transfer learning capabilities of these models by exposing them to
out-of-distribution (OOD) samples. These include deviations from standard
system operations, while the core dynamics of the underlying physical system
are preserved. Additionally, we test how well the models respond to several
data augmentation techniques, including added noise and time warping. Our
experimental framework utilizes a simulated three-tank system, proposed as a
novel benchmark for evaluating the robustness and generalization performance of
DL algorithms in CPS data contexts. The findings reveal that certain DL model
architectures and training techniques exhibit superior effectiveness in
handling OOD samples and various perturbations. These insights have significant
implications for the development of DL models that deliver reliable and robust
performance in real-world CPS applications.Comment: Accepted at the IJCAI 2023 Workshop of Artificial Intelligence for
Time Series Analysis (AI4TS
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
A Research Agenda for AI Planning in the Field of Flexible Production Systems
Manufacturing companies face challenges when it comes to quickly adapting
their production control to fluctuating demands or changing requirements.
Control approaches that encapsulate production functions as services have shown
to be promising in order to increase the flexibility of Cyber-Physical
Production Systems. But an existing challenge of such approaches is finding a
production plan based on provided functionalities for a demanded product,
especially when there is no direct (i.e., syntactic) match between demanded and
provided functions. While there is a variety of approaches to production
planning, flexible production poses specific requirements that are not covered
by existing research. In this contribution, we first capture these requirements
for flexible production environments. Afterwards, an overview of current
Artificial Intelligence approaches that can be utilized in order to overcome
the aforementioned challenges is given. For this purpose, we focus on planning
algorithms, but also consider models of production systems that can act as
inputs to these algorithms. Approaches from both symbolic AI planning as well
as approaches based on Machine Learning are discussed and eventually compared
against the requirements. Based on this comparison, a research agenda is
derived