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Model for the Selection of Predictive Maintenance Techniques

Authors: María Del Carmen Carnero Moya;

Model for the Selection of Predictive Maintenance Techniques

Abstract

Abstract A Predictive Maintenance Program can provide significant benefits to industrial plants in the areas of security, quality and availability. However, Predictive Maintenance Programs have lacked analysis in matters related to their set up, management and control. In this paper, a model is proposed in order to select the most suitable predictive technique to set up in an industrial plant. For this purpose, different alternatives are proposed depending on the technological level of the Predictive Maintenance Program. The condition monitoring techniques considered in the model are: vibration analysis and lubricant analysis; the integration of both techniques is also considered in the construction of alternatives. The proposed model can facilitate the decision making process for setting up Predictive Maintenance programs, and also help to avoid the failure of such programs. The model has been tested in a set of industrial plants with a Predictive Maintenance Program.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Average
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