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• W2990029379 abstract "Equipment control represents a critical aspect of asset management because of its impact on asset’s reliability. Monitoring the behavior of the asset and being able to anticipate the occurrence of critical situation like asset’s failures play a vital role in the success of a company. Hence, in this paper, we propose a decision support system based on the development of an Artificial Neural Network and Association Rule Mining. The aim of this work is supporting the Operations and Maintenance managers in defining the best control policy on asset’s behavior, in order to anticipate the occurrence of failing situations and find relations among operating conditions’ values helpful for failures prediction and recognition. Implementing the proposed approach may support the decision maker in defining the best maintenance policy for the asset, knowing in advance the conditions leading to its failure. A brief example-case is provided for the discussion of the practical implications of the proposed approach." @default.
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• W2990029379 date "2019-10-01" @default.
• W2990029379 modified "2023-10-03" @default.
• W2990029379 title "Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining" @default.
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• W2990029379 doi "https://doi.org/10.1109/smc.2019.8914578" @default.
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