The already completed research project FEE developed new assistance systems to support plant operators in critical situations by detecting critical situations in near real-time and to predict and prevent critical situations.

The abbreviation FEE stands for: (German) Frühzeitige Erkennung und Entscheidungsunterstützung für kritische Situationen im Produktionsumfeld / Early Detection of and Decision Support for Critical Situations in Production Environments

Project Idea

A high automation degree of processing plants allows economical operations even in countries with high labor costs such as Germany. However, it reduces the experience of the operators regarding the process dynamics and can lead to information overload in critical situations (due to “alarm flood”). When control is lost human lifes and environment are endangered. This can cause serious damage to assets and costly production downtime.

The goal of the BMBF research project FEE is therefore to detect critical situations in the plant at an early stage, and to develop assistance functions that support plant operators in decision making during critical situations.

For this purpose appropriate real-time big data methods will be developed that will utilize the available heterogenous mass data from the plants. Early warnings will be provided to the operator in order to enable proactive instead of reactive actions. Furthermore, assistance functions will be developed that support the operators in deciding on their intervention strategy.

Results

KDML Conference 2016 (Poster)

Atzmüller, M. Schmidt, A., Arnu, D.: Sequential Modeling and Structural Anomaly Analytics in Industrial Production Environments.

atp edition 58(9) (2016) (Journal submission)

Atzmüller, M., Klöpper, B., Mawla, H.A., Jäschke, B., Hollender, M., Graube, M., Arnu,D., Schmidt, A., Heinze, S., Schorer, L., Kroll, A., Stumme, G., Urbas, L.: Big Data Analytics for Proactive Industrial Decision Support: Approaches First Experiences in the Context of the FEE Project.

LWDA 2017 (Paper)

Atzmüller, M. Schmidt, A., Arnu, D.: Sequential Modeling and Structural Anomaly Analytics in Industrial Production Environments.

Project Information

Project Duration

September 1st, 2014 - December 31, 2017

Funding Grant Number

01IS14006E

Project Sponsor

The project is funded by the German Federal Ministry of Education and Research (BMBF) under the “ICT 2020” - Research for Innovation

Project Website

https://www.fee-projekt.de/index_en.html

Project Partners

• RapidMiner GmbH
• ABB AG
• TU Dresden
  • Professur für Prozessleittechnik
• Universität Kassel
  • Fachgebiet Mess- und Regelungstechnik
  • Fachgebiet Wissensverarbeitung, Forschungszentrum für Informationstechnik-Gestaltung
• INEOS
  • Fachgruppe Process Control Application Engineering
• BASF SE
  • Abteilung Automation Technology/Engineering & Maintenance
• PCK
  • Abteilung Anlagenautomation