Industrial signal processing, Pattern recognition

AutASS: Autonome Antriebstechnik durch Sensorfusion für die intelligente, simulationsbasierte Überwachung & Steuerung von Produktionsanlagen

01.02.2010 bis 31.05.2013

A trend towards more and more complex systems is observable worldwide. Because of the prevailing diversity of topics, solution concepts are often investigated isolated which leads to complexity problems. In the scope of intelligent drive systems for machines or facilities, cognitive approaches can be found many times. Nevertheless, it is noticeable that integral concepts and realisations of such systems for process automation and production technology are still in an early stage or in the state of R&D, respectively, although practicable partly solutions are available in the market. Necessary tools such as algorithmic procedures, sensory systems, development methods, test facilities and production technologies are available in different depths. Nevertheless, application specific toolsets with the necessary adjustments and additions, respectively, to develop and realise industrially applicable drive systems are missing.

The aim of this project is to integrate sensor functionality in electrical drives for creating intelligent, autonomous self-diagnostic capabilities of single components of the drive system and the process and therefore the realisation of mechatronic control circuits. The “health status” (wear and tear, life cycle prognosis) of electrical drives including subsequent processes is determined in good time and reliable by evaluating test signals with the help of combining flexible, modular sensory functions. All sensory units are wirelessly connected with the “intelligent electronics” of the drive. The algorithms applied in there ensure the autonomous function of the “intelligent electronics” by signal analysis capabilities. For executing evaluations as well as providing monitoring control and presenting results, the connected diagnosis center (trend analysis, threshold value definitions, damage prognoses, etc.) is available.

This project belongs to the research scope “Internet of Things” and is funded by the initiative Autonomic Systems of the Federal Ministry of Economics and Technology.

This project is promoted by:
Bundesministerium für Wirtschaft und Energie (BMWi)
Sponsors: Das Deutsche Zentrum für Luft- und Raumfahrt e.V. (DLR)
Funding Code: 01MA09061
Funding Lines: AUTONOMIK für Industrie 4.0
Stakeholders / Contacts: Prof. Dr.-Ing. Volker Lohweg, Alexander Dicks, M. Sc.
Employees: Alexander Dicks, M. Sc., Dr.-Ing. Uwe Mönks, Martyna Bator, B. Sc., Prof. Dr. rer. nat. Oliver Niggemann
Martyna Bator, B. Sc., Alexander Dicks, M. Sc., Dr.-Ing. Uwe Mönks, Prof. Dr.-Ing. Volker Lohweg
Feature Extraction and Reduction Applied to Sensorless Drive Diagnosis
In: 22. Workshop Computational Intelligence, Dec 2012
Holger Hähnel, Arne-Jens Hempel, Dr.-Ing. Uwe Mönks, Prof. Dr.-Ing. Volker Lohweg
Integration of Statistical Analyses for Parametrisation of the Fuzzy Pattern Classification
In: 22. Workshop Computational Intelligence, Dec 2012
Christian Bayer, Martyna Bator, B. Sc., Olaf Enge-Rosenblatt, Dr.-Ing. Uwe Mönks, Alexander Dicks, M. Sc., Prof. Dr.-Ing. Volker Lohweg
Sensorless Drive Diagnosis Using Automated Feature Extraction, Significance Ranking and Reduction
In: 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2013
Fabian Paschke, Daniel Bayer, Martyna Bator, B. Sc., Dr.-Ing. Uwe Mönks, Alexander Dicks, M. Sc., Olaf Enge-Rosenblatt, Prof. Dr.-Ing. Volker Lohweg
Sensorlose Zustandsüberwachung an Synchronmotoren.
In: 23. Workshop Computational Intelligence, Dec 2013
Fraunhofer IIS/EAS
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