Intelligent maintenance for industrial processes, a case study on cold stamping
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2018
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Springer Verlag
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Abstract
The correct diagnosis of tool breakage is fundamental to improve productivity, minimizing the number of unproductive hours and avoiding expensive repairs. The use of Data Mining techniques provides a significant added value in terms of improvements in the robustness, reliability and flexibility of the monitored systems. In this work, a general view of a diagnosis and prognosis of tool breakage in Industrial Processes is proposed. The important issues identified will be analyzed: filtering, process characterization and data based modeling. A case study has been implemented to carry out the prognosis of tool breakage in the cold stamping process. The results provided are qualitative trends and hypothesis to perform the prognosis. Although a validation in real operation is needed, these results are promising and demonstrate the goodness of using these type of techniques in real processes.
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Publisher Copyright: © 2018, Springer International Publishing AG.
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Boto , F , Lizuain , Z & Cortadi , A J 2018 , Intelligent maintenance for industrial processes, a case study on cold stamping . in H Perez Garcia , J Alfonso-Cendon , L Sanchez Gonzalez , E Corchado & H Quintian (eds) , International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings . Advances in Intelligent Systems and Computing , vol. 649 , Springer Verlag , pp. 157-166 , International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017 , Leon , Spain , 6/09/17 . https://doi.org/10.1007/978-3-319-67180-2_15
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