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Big Data Technology for Resilient Failure Management in Production Systems

  • Due to a growing complexity within value chains the susceptibility to failures in production processes increases. The research project BigPro explores the applicability of Big Data to realize a pro-active failure management in production systems. The BigPro-platform complements structured production data and unstructured human data to improve failure management. In a novel approach, the aggregated data is analyzed for reoccurring patterns that indicate possible failures of the production system, known from historic failure events. These patterns are linked to failures and respective countermeasures and documented in a catalog. The project results are validated in three industrial use cases.

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Metadaten
Author:Volker StichORCiDGND, Felix JordanGND, Martin Birkmeier, Kerem Oflazgil, Jan Reschke, Anna Diews
DOI:https://doi.org/10.1007/978-3-319-22756-6_55
Parent Title (English):Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth. IFIP WG 5.7 International Conference, APMS 2015, Tokyo, Japan, September 7-9, 2015, Proceedings, Part I.
Publisher:APMS
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2024/06/21
Date of first Publication:2015/10/07
Release Date:2025/09/10
Tag:Complex event processing; Failure management; Pattern management; Production control
GND Keyword:Big DataGND
First Page:447
Last Page:454
Note:
BigPro: Big-Data and event-based adjustment for the configuration of resilient production systems

Project goal is the development of a Big Data platform including algorithms for data pattern detection to implement a proactivce disturbance management system in the production. Human data is considered as an additional data source. The disturbance management is further supported by an visualization of the disturbances and respective counteractions.

Duration: 01.09.2014 – 30.11.2017
Funding no.: 01IS14011
Funding: Bundesministerium für Bildung und Forschung (BMBF)
Promoters: DLR Projektträger

Project partners:
    Asseco Solutions AG, Karlsruhe
    AUTO HEINEN GmbH, Bad Münstereifel
    cognesys gmbh, Aachen
    DFA Demonstrationsfabrik Aachen GmbH, Aachen
    EICe Aachen GmbH, Aachen
    EML European Media Laboratory GmbH, Heidelberg
    FZI Forschungszentrum Informatik am Karlsruher Institut für Technologie, Karlsruhe
    i2solutions GmbH, Stolberg
    Robert Bosch GmbH, Gerlingen-Schillerhöhe
    Software AG, Darmstadt
    Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen
FIR-Number:SV6568
Date of the presentation:07.10.2015
Name of the conference:Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth. IFIP WG 5.7 International Conference, APMS 2015
place of the conference:Tokyo
Institute / Department:FIR e. V. an der RWTH Aachen
Informationsmanagement
Produktionsmanagement
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften

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