Integrated Production and Maintenance Planning for Cyber-physical Production Systems

M. Schreiber; J. Klöber-Koch; C. Richter; G. Reinhart

doi:10.1016/j.procir.2018.03.144

Integrated Production and Maintenance Planning for Cyber-physical Production Systems

M. Schreiber, J. Klöber-Koch, C. Richter, G. Reinhart

Fraunhofer Institute for Casting, Composite and Processing Technology IGCV

Research output: Contribution to journal › Conference article › peer-review

14 Scopus citations

Abstract

Continuously rising production costs and efficiency requirements present challenges for manufacturing companies. One way of overcoming these challenges is to improve maintenance efficiency and effectiveness by developing and integrating predictive maintenance tools, and using this information for the targeted planning of maintenance measures. However, the integration of sensors into previously-installed manufacturing resources for predicting the necessary maintenance tasks is one of the main challenges facing manufacturing companies. Therefore, this paper presents an innovative methodology for predictive maintenance tools as intelligent cloud services and the industrial application of this methodology for an integrated production and maintenance planning.

Original language	English
Pages (from-to)	934-939
Number of pages	6
Journal	Procedia CIRP
Volume	72
DOIs	https://doi.org/10.1016/j.procir.2018.03.144
State	Published - 2018
Externally published	Yes
Event	51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 - Stockholm, Sweden Duration: 16 May 2018 → 18 May 2018

Keywords

Decision Support System
Maintenance
Methodology

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10.1016/j.procir.2018.03.144

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title = "Integrated Production and Maintenance Planning for Cyber-physical Production Systems",

abstract = "Continuously rising production costs and efficiency requirements present challenges for manufacturing companies. One way of overcoming these challenges is to improve maintenance efficiency and effectiveness by developing and integrating predictive maintenance tools, and using this information for the targeted planning of maintenance measures. However, the integration of sensors into previously-installed manufacturing resources for predicting the necessary maintenance tasks is one of the main challenges facing manufacturing companies. Therefore, this paper presents an innovative methodology for predictive maintenance tools as intelligent cloud services and the industrial application of this methodology for an integrated production and maintenance planning.",

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author = "M. Schreiber and J. Kl{\"o}ber-Koch and C. Richter and G. Reinhart",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors. Published by Elsevier B.V.; 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 ; Conference date: 16-05-2018 Through 18-05-2018",

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T1 - Integrated Production and Maintenance Planning for Cyber-physical Production Systems

AU - Schreiber, M.

AU - Klöber-Koch, J.

AU - Richter, C.

AU - Reinhart, G.

PY - 2018

Y1 - 2018

N2 - Continuously rising production costs and efficiency requirements present challenges for manufacturing companies. One way of overcoming these challenges is to improve maintenance efficiency and effectiveness by developing and integrating predictive maintenance tools, and using this information for the targeted planning of maintenance measures. However, the integration of sensors into previously-installed manufacturing resources for predicting the necessary maintenance tasks is one of the main challenges facing manufacturing companies. Therefore, this paper presents an innovative methodology for predictive maintenance tools as intelligent cloud services and the industrial application of this methodology for an integrated production and maintenance planning.

AB - Continuously rising production costs and efficiency requirements present challenges for manufacturing companies. One way of overcoming these challenges is to improve maintenance efficiency and effectiveness by developing and integrating predictive maintenance tools, and using this information for the targeted planning of maintenance measures. However, the integration of sensors into previously-installed manufacturing resources for predicting the necessary maintenance tasks is one of the main challenges facing manufacturing companies. Therefore, this paper presents an innovative methodology for predictive maintenance tools as intelligent cloud services and the industrial application of this methodology for an integrated production and maintenance planning.

KW - Decision Support System

KW - Maintenance

KW - Methodology

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U2 - 10.1016/j.procir.2018.03.144

DO - 10.1016/j.procir.2018.03.144

M3 - Conference article

AN - SCOPUS:85049564403

SN - 2212-8271

VL - 72

SP - 934

EP - 939

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018

Y2 - 16 May 2018 through 18 May 2018

ER -

Integrated Production and Maintenance Planning for Cyber-physical Production Systems

Abstract

Keywords

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