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

| 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 in Industrial Production Settings. In: EDBT/ICDT 2019 Workshops, Lisbon, 26 March 2019, CEUR Workshop Proceedings 2322, CEUR-WS.org.

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 11614, Springer, pp. 63-70.

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 11614, Springer, pp. 371-378.

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 11614, Springer, pp. 43-50.

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, Vol. 7, pp. 71921-71932, 10.1109/ACCESS.2019.2919162.

Hoppenstedt, Burkhard and Reichert, Manfred and Kammerer, Klaus and Probst, Thomas and Schlee, Winfried and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data. Sensors, MDPI, Vol. 19, pp. 3303, 10.3390/s19183903.

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.

Hoppenstedt, Burkhard and Pryss, Rüdiger and Kammerer, Klaus and Reichert, Manfred (2018) CONSENSORS: A Neural Network Framework for Sensor Data Analysis. In: OTM 2018 Workshops, Valetta, Malta, October 22-26, LNCS 11231, Springer, pp. 196-200.

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

| 2019 | 2018 | 2017 |

2019

Allgaier, Johannes (2019) Machine learning under concept drift for industrial data using Python. Master thesis, Institute of Databases and Informations Systems.

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

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.