Innovative Modeling Approaches for Production Systems to raise validatable efficiency

Informazioni relative al progetto

IMPROVE

ID dell’accordo di sovvenzione: 678867

DOI 10.3030/678867

Progetto chiuso

Data della firma CE 3 Agosto 2015

Data di avvio 1 Settembre 2015

Data di completamento 31 Agosto 2018

Finanziato da

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

Costo totale € 4 148 554,00

Contributo UE € 4 148 554,00

Coordinato da

TECHNISCHE HOCHSCHULE OSTWESTFALEN-LIPPE
Germany

Risultati finali

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

Pubblicazioni

Conference proceedings (9)

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

Autori: Emanuel Trunzer, Iris Kirchen, Jens Folmer, Gennadiy Koltun, Birgit Vogel-Heuser
Pubblicato in: 2017 IEEE International Conference on Industrial Technology (ICIT), Numero 22-25 March 2017, 2017, Pagina/e 1106-1111, ISBN 978-1-5090-5320-9
Editore: 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

Autori: Dorothea Pantförder, Julia Schaupp, Birgit Vogel-Heuser
Pubblicato in: HCI International 2017 – Posters' Extended Abstracts, Numero Vol. 713, 2017, Pagina/e 358-365, ISBN 978-3-319-58749-3
Editore: 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

Autori: Ezdin Aslanci, Kutalmis Coskun, Peter Schuller, Borahan Tumer
Pubblicato in: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Numero 24-26 July 2017, 2017, Pagina/e 787-792, ISBN 978-1-5386-0837-1
Editore: IEEE
DOI: 10.1109/INDIN.2017.8104873

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

Autori: Bans Gun Surmeli, Feyza Eksen, Bilal Dinc, Peter Schuller, Borahan Tumer
Pubblicato in: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Numero 24-26 July 2017, 2017, Pagina/e 841-846, ISBN 978-1-5386-0837-1
Editore: IEEE
DOI: 10.1109/INDIN.2017.8104881

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

Autori: Benedikt Eiteneuer; Oliver Niggemann
Pubblicato in: DX18 29th International Workshop on Principles of Diagnostics, Numero 1, 2018
Editore: DX
DOI: 10.5281/zenodo.1409641

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

Autori: Alexander von Birgelen, Oliver Niggemann
Pubblicato in: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Numero 12-15 September 2017, 2017, Pagina/e 1-8, ISBN 978-1-5090-6505-9
Editore: IEEE
DOI: 10.1109/ETFA.2017.8247695

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

Autori: Claudio Santo Longo, Cesare Fantuzzi
Pubblicato in: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2017, Pagina/e 1-8, ISBN 978-1-5090-6505-9
Editore: IEEE
DOI: 10.1109/ETFA.2017.8247582

Defining and validating similarity measures for industrial alarm flood analysis

Autori: Marta Fullen, Peter Schuller, Oliver Niggemann
Pubblicato in: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Numero 24-26 July 2017, 2017, Pagina/e 781-786, ISBN 978-1-5386-0837-1
Editore: IEEE
DOI: 10.1109/INDIN.2017.8104872

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

Autori: Khaled Al-Gumaei, Kornelia Schuba, Andrej Friesen, Sascha Heymann, Carsten Pieper, Florian Pethig, and Sebastian Schriegel
Pubblicato in: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, Numero 1536098400, 2018
Editore: IEEE
DOI: 10.5281/zenodo.1446427

Peer reviewed articles (1)

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

Autori: Eray Gençay, Peter Schüller, Esra Erdem
Pubblicato in: Journal of Intelligent Manufacturing, 2017, Pagina/e 1-16, ISSN 0956-5515
Editore: 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

Autori: Oliver Niggemann, Peter Schüller
Pubblicato in: Technologien für die intelligente Automation, 2018, ISBN 978-3-662-57805-6
Editore: Springer Berlin Heidelberg
DOI: 10.1007/978-3-662-57805-6