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6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07) 2007
DOI: 10.1109/cisim.2007.14
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Architecture of a Predictive Maintenance Framework

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Cited by 32 publications
(22 citation statements)
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“…In addition, they introduced previous case studies in system monitoring and control. Furthermore, Grobal et al [13] introduced the initial architecture for CBM framework, which was being realized in a joint project with SAP research. They mentioned several aspects of CBM: identification of indicators, measurement of indicators, modeling of indicators, forecasting of indicators, and decision making.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In addition, they introduced previous case studies in system monitoring and control. Furthermore, Grobal et al [13] introduced the initial architecture for CBM framework, which was being realized in a joint project with SAP research. They mentioned several aspects of CBM: identification of indicators, measurement of indicators, modeling of indicators, forecasting of indicators, and decision making.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The informational basis for the prognosis of future failures can also be obtained from statistical data about the equipment. However, actual condition information yields more accurate prognoses [15]. The global objective of maintenance depend on a mandatory collaboration (inter and intracompany processes integration), mandatory synchronization of knowledge between all the objects-actors-systems involved in the maintenance process all along the various phases of the product life cycle (human and/or automated actors) [1].…”
Section: Activities For Condition Based Maintenancementioning
confidence: 99%
“…The motivation for this discussion is the implementation of a framework for predictive maintenance as presented in [3]. The aim here is to integrate field devices into the framework with the fewest possible manual configuration effort.…”
Section: Introductionmentioning
confidence: 99%