pdmInsight – Predictive Maintenance Applications

Project Description

Predictive maintenance (PDM) is a method designed to find an optimal maintenance cycle for machines. Therefore, sensor values are included to find correlations between measured data and machine behavior.

This interdisciplinary approach includes knowledge about engineering, data mining, and management. We try to enrich manufacturing execution systems (MES) with the features of predictive maintenance for an advanced production control. The research focus is to include generic methods into production processes.

To connect machines to predictive maintenance application it is necessary to deal with machine communication protocols like OPC UA and MQTT. These techniques allow not only the information transfer but also the semantical modeling of machines.

Considering growing data logging, it is necessary to include data reduction techniques and distributed analyses. All collected information is used to train the application and forecast machine behavior.

Project Details

Project Team

Ulm University
	Burkhard Hoppenstedt Ulm University, Institute of Databases and Information Systems
	Dr. Rüdiger Pryss Ulm University, Institute of Databases and Information Systems
	Klaus Kammerer Ulm University, Institute of Databases and Information Systems
	Prof. Dr. Manfred Reichert Ulm University, Institute of Databases and Information Systems

Project Partners

Ulm University, Institute of Databases and Information Systems

atr Software GmbH

Funding

The project is partially funded by atr Software GmbH.

Duration

The pdmInsight project has been running since 2016.

Publications

| 2018 | 2017 |

2018

Hoppenstedt, Burkhard and Pryss, Rüdiger and Stelzer, Birgit and Meyer-Brötz, Fabian and Kammerer, Klaus and Treß, Alexander and Reichert, Manfred (2018) Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems - A Bibliometric Analysis . Applied Sciences, MDPI, Vol. 8, pp. 1-29, 10.3390/app8060916.

2017

Hoppenstedt, Burkhard and Pryss, Rüdiger and Treß, Alexander and Biechele, Bernd and Reichert, Manfred (2017) Datengetriebene Module für Predictive Maintenance. ProductivITy, Vol. 22, pp. 21-23.

Master & Bachelor Theses

| 2018 | 2017 |

2018

Väth, Thomas (2018) Exploring Temporal Data in a Mixed-Reality Application. Bachelor thesis, Ulm University.

2017

Grabiec, Sebastian (2017) Developing a client-specific workflow for Predictive Maintenance. Master thesis, Ulm University.

Grausz, Krisztián (2017) Log Analyzer for IoT Applications. Master thesis, Ulm University.

Kuhaupt, Nicolas (2017) Conception And Analysis Of A Raspberry Pi Cluster With Apache Spark. Master thesis, Ulm University.

Salonikidis, Georgios (2017) Minimization of Redundant Make-To-Stock Production in a Dental Factory: An Integer Linear Programming Approach. Bachelor thesis, Ulm University.

Schwarz, Holger (2017) Evaluierung neuronaler Netze auf Maschinendatenbasis. Master thesis, Ulm University.