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

| 2019 | 2018 | 2017 |

2019

Hoppenstedt, Burkhard and Reichert, Manfred and Kammerer, Klaus and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Towards a Hierarchical Approach for Outlier Detection inIndustrial Production Settings. In: EDBT/ICDT 2019 Workshops, Lisbon, 26 March 2019, CEUR Workshop Proceedings 2322, CEUR-WS.org. file
Hoppenstedt, Burkhard and Kammerer, Klaus and Reichert, Manfred and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), Santa Maria al Bagno, Italy, June 24-27, 2019, Lecture Notes in Computer Science, Springer. (Accepted for Publication) file
Hoppenstedt, Burkhard and Schmid, Michael and Kammerer, Klaus and Scholta, Joachim and Reichert, Manfred and Pryss, Rüdiger (2019) Analysis of Fuel Cells Utilizing Mixed Reality and IoT Achievements. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), Santa Maria al Bagno, Italy, June 24-27, 2019, Lecture Notes in Computer Science, Springer. (Accepted for Publication) file
Hoppenstedt, Burkhard and Witte, Thomas and Ruof, Jona and Kammerer, Klaus and Tichy, Matthias and Reichert, Manfred and Pryss, Rüdiger (2019) Debugging Quadrocopter Trajectories in Mixed Reality. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), Santa Maria al Bagno, Italy, June 24-27, 2019, Lecture Notes in Computer Science, Springer. (Accepted for Publication) file
Hoppenstedt, Burkhard and Probst, Thomas and Reichert, Manfred and Schlee, Winfried and Kammerer, Klaus and Spiliopoulou, Myra and Schobel, Johannes and Winter, Michael and Felnhofer, Anna and Kothgassner, Oswald and Pryss, Rüdiger (2019) Applicability of Immersive Analytics in Mixed Reality: Usability Study . IEEE Access, pp. 71921-71932, 10.1109/ACCESS.2019.2919162.

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
Hoppenstedt, Burkhard and Pryss, Rüdiger and Kammerer, Klaus and Reichert, Manfred (2018) CONSENSORS: A Neural Network Framework for Sensor Data Analysis. In: 26th International Conference on Cooperative Information Systems (CoopIS 2018)), Valetta, Malta, October 22-26, LNCS, Springer. (Accepted for Publication) 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

| 2019 | 2018 | 2017 |

2019

Berroth, Kai-Uwe (2019) Evaluation von Vorhersagemodellen auf Basis von UN Millenniumszielen. Master thesis, Institute of Databases and Informations Systems. file

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