Department 5: Electrical Engineering and Computer Science - Embedded SW-Engineering

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ASIIN accredited

ASIIN accredited

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Presentations for the MIT seminar and the SMW lecture are on January 20th from 10-3pm in the meeting room in the CIIT.

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Our Research Focus: System Analysis

Our main research area is the application of data mining algorithms to the analysis and improvement of technical systems such as automation systems, production plants, or embedded software systems. Examples are the monitoring of complex production plants, the diagnosis of network communication, or the development of easy-redesignable modular automation systems.

As shown in the figure below, most of our solutions comprise the following steps:

Step 1: Data Integration


In most systems, important information is spread through-out the systems, i.e. data must be integrated first to allow for the application of data analysis and data mining algorithms. For this, we apply and develop real-time middleware software and data repositories. Data integration normally comprises both horizontal integration (e.g. middlewares for Profinet, AUTOSAR RTE) and vertical integration (e.g. OPC (UA), Webservices). In all cases, the data integration should not rely on manual implementation efforts, i.e. it must be transparent for the function developer.

To integrate data from different sources, all data must be interpreted in relation to their function within the overall system. For this, we use system models such as AutomationML, AUTOSAR, IEC 61131.

Step 2: System Analysis


Once the status of the overall system is known, analysis questions can be answered, e.g. :
  • Is the system behaving normally?
  • Is the timing of the system correct?
  • Which errors have occurred in the systems?
  • Which system components are erroneous and must therefore be replaced?
  • Has the behavior of system components degenerated, i.e. is a maintenance of some components recommendable?
  • Can the performance of the system be improved, e.g. by re-designing the system or by re-scheduling jobs?
For this, system simulations and algorithms from the field of artificial intelligence are used: To detect non-normal system behavior, it is often necessary to compare the current system behavior to a model of the normal behavior. This so-called anomaly detection therefore relies on the modeling and simulation of the normal system behavior. For this, we use modeling approaches such as Modelica or Simulink. Often, based on the discovered anomalies, error causes are identified using methods such as model-based diagnosis.

Currently such approaches are improved by the learning of normal behavior: For complex systems, often a manual definition of the normal system behavior is not possible. To overcome this modeling bottleneck, we apply model learning algorithms such as automata learning, support vector machines, statistical learning, or clustering.