SmarterMaintenance

Increasing demands on the reliability of production lines nowadays also place high demands on the maintenance of machines and plants. Due to high costs of skilled workers, lack of networking to problem solving information and high material costs for spare parts, maintenance is e.g. in the steel industry the third largest cost block and cause for several days of production downtime per year. 

Federal Ministry of Education and Research 

As part of the industrial progress towards Industry 4.0, the new analysis tool "SmarterMaintenance" is intended to fundamentally optimize the maintenance of process plants. By evaluating sensor data with complex data mining analysis, a connection between measured values is to be detected in a self-learning manner and predictive maintenance is to be made possible on the basis of this. Furthermore, a text mining procedure based on a semantic model with keyword extraction and part-of-speech analysis realizes the automated evaluation and logical information linking of operating instructions, etc. The new tool is to be able to automatically detect a connection between measured values by evaluating sensor data. The new tool should be able to reduce maintenance costs and downtime in production lines by 30% to 50%.

Within the scope of its own subproject, the Department of Information Systems is developing a text evaluation procedure based on a semantic model as well as keyword extraction and part-of-speech analysis procedures. With the help of this algorithm it will be possible to link machine problem information with corresponding solution information from electronic documents.

The SmarterMaintenance project is funded by the German Federal Ministry for Economic Affairs and Energy under the project sponsorship of the AiF.

Funding code: ZF4189701BZ5

Duration: 01.05.2016 - 31.10.2017

Project participants:

eoda GmbH(https://www.eoda.de/de/)

Contact

• Dr. Philipp Bitzer

QuAALi