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
Opens internal link in current windowBurkhard Hoppenstedt
Ulm University, Institute of Databases and Information Systems
Opens internal link in current windowDr. Rüdiger Pryss
Ulm University, Institute of Databases and Information Systems
Opens internal link in current windowKlaus Kammerer
Ulm University, Institute of Databases and Information Systems
Opens internal link in current windowProf. Dr. Manfred Reichert
Ulm University, Institute of Databases and Information Systems

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. file

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. file

Master & Bachelor Theses

| 2018 | 2017 |

2018

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

2017

Grabiec, Sebastian (2017) Developing a client-specific workflow for Predictive Maintenance. Master thesis, Ulm University. file
Grausz, Krisztián (2017) Log Analyzer for IoT Applications. Master thesis, Ulm University. file
Kuhaupt, Nicolas (2017) Conception And Analysis Of A Raspberry Pi Cluster With Apache Spark. Master thesis, Ulm University. file
Salonikidis, Georgios (2017) Minimization of Redundant Make-To-Stock Production in a Dental Factory: An Integer Linear Programming Approach. Bachelor thesis, Ulm University. file
Schwarz, Holger (2017) Evaluierung neuronaler Netze auf Maschinendatenbasis. Master thesis, Ulm University. file