Innovative Modeling Approaches for Production Systems to raise validatable efficiency

Informacje na temat projektu

IMPROVE

Identyfikator umowy o grant: 678867

DOI 10.3030/678867

Projekt został zamknięty

Data podpisania przez KE 3 Sierpnia 2015

Data rozpoczęcia 1 Września 2015

Data zakończenia 31 Sierpnia 2018

Finansowanie w ramach

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Koszt całkowity € 4 148 554,00

Wkład UE € 4 148 554,00

Koordynowany przez

TECHNISCHE HOCHSCHULE OSTWESTFALEN-LIPPE
Germany

Rezultaty

Documents, reports (14)

Report on the overall architecture which ensures the collaboration of all WPs
Framework for operator knowledge collection
Summary of all requirements for all parts of the project
An algorithm to classify an anomaly as critical or not
Evaluate the forecasting quality
Quality based HMI, which supports decision making in problematic production scenarios
An algorithm detecting anomalies out of observation based on learned models
Behaviour model which is learned
Learned causality model which integrates expert knowledge
Toolbox to understand the effects of the transition from traditional to smart production. Design learning scenarios and on-the-job training formats that help users to skilfully operate the system and to modify sociotechnical arrangements
Open access summary report
Evaluate the results of the pilot runs of three demonstrators and the numerical benefit
An intelligent optimization algorithm which is able to find the efficient configuration of the factory
An algorithm to identifying the root cause of a fault

Demonstrators, pilots, prototypes (2)

Transfer the implementation from the lab demonstrators to three prototypes in different industries
The implemented algorithms are validated on lab demonstrators

Other (1)

External project website

Publikacje

Conference proceedings (9)

A flexible architecture for data mining from heterogeneous data sources in automated production systems

Autorzy: Emanuel Trunzer, Iris Kirchen, Jens Folmer, Gennadiy Koltun, Birgit Vogel-Heuser
Opublikowane w: 2017 IEEE International Conference on Industrial Technology (ICIT), Numer 22-25 March 2017, 2017, Strona(/y) 1106-1111, ISBN 978-1-5090-5320-9
Wydawca: IEEE
DOI: 10.1109/ICIT.2017.7915517

Making Implicit Knowledge Explicit – Acquisition of Plant Staff’s Mental Models as a Basis for Developing a Decision Support System

Autorzy: Dorothea Pantförder, Julia Schaupp, Birgit Vogel-Heuser
Opublikowane w: HCI International 2017 – Posters' Extended Abstracts, Numer Vol. 713, 2017, Strona(/y) 358-365, ISBN 978-3-319-58749-3
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-319-58750-9_50

Detection of regime switching points in non-stationary sequences using stochastic learning based weak estimation method

Autorzy: Ezdin Aslanci, Kutalmis Coskun, Peter Schuller, Borahan Tumer
Opublikowane w: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Numer 24-26 July 2017, 2017, Strona(/y) 787-792, ISBN 978-1-5386-0837-1
Wydawca: IEEE
DOI: 10.1109/INDIN.2017.8104873

Unsupervised mode detection in cyber-physical systems using variable order Markov models

Autorzy: Bans Gun Surmeli, Feyza Eksen, Bilal Dinc, Peter Schuller, Borahan Tumer
Opublikowane w: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Numer 24-26 July 2017, 2017, Strona(/y) 841-846, ISBN 978-1-5386-0837-1
Wydawca: IEEE
DOI: 10.1109/INDIN.2017.8104881

LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems

Autorzy: Benedikt Eiteneuer; Oliver Niggemann
Opublikowane w: DX18 29th International Workshop on Principles of Diagnostics, Numer 1, 2018
Wydawca: DX
DOI: 10.5281/zenodo.1409641

Using self-organizing maps to learn hybrid timed automata in absence of discrete events

Autorzy: Alexander von Birgelen, Oliver Niggemann
Opublikowane w: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Numer 12-15 September 2017, 2017, Strona(/y) 1-8, ISBN 978-1-5090-6505-9
Wydawca: IEEE
DOI: 10.1109/ETFA.2017.8247695

Simulation and optimisation of production lines in the framework of the IMPROVE project

Autorzy: Claudio Santo Longo, Cesare Fantuzzi
Opublikowane w: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2017, Strona(/y) 1-8, ISBN 978-1-5090-6505-9
Wydawca: IEEE
DOI: 10.1109/ETFA.2017.8247582

Defining and validating similarity measures for industrial alarm flood analysis

Autorzy: Marta Fullen, Peter Schuller, Oliver Niggemann
Opublikowane w: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Numer 24-26 July 2017, 2017, Strona(/y) 781-786, ISBN 978-1-5386-0837-1
Wydawca: IEEE
DOI: 10.1109/INDIN.2017.8104872

A Survey of Internet of Things and Big Data Integrated Solutions for Industrie 4.0

Autorzy: Khaled Al-Gumaei, Kornelia Schuba, Andrej Friesen, Sascha Heymann, Carsten Pieper, Florian Pethig, and Sebastian Schriegel
Opublikowane w: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, Numer 1536098400, 2018
Wydawca: IEEE
DOI: 10.5281/zenodo.1446427

Peer reviewed articles (1)

Applications of non-monotonic reasoning to automotive product configuration using answer set programming

Autorzy: Eray Gençay, Peter Schüller, Esra Erdem
Opublikowane w: Journal of Intelligent Manufacturing, 2017, Strona(/y) 1-16, ISSN 0956-5515
Wydawca: Kluwer Academic Publishers
DOI: 10.1007/s10845-017-1333-3

Monographic books (1)

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency - Intelligent Methods for the Factory of the Future

Autorzy: Oliver Niggemann, Peter Schüller
Opublikowane w: Technologien für die intelligente Automation, 2018, ISBN 978-3-662-57805-6
Wydawca: Springer Berlin Heidelberg
DOI: 10.1007/978-3-662-57805-6