TY - GEN
T1 - Intelligent maintenance for industrial processes, a case study on cold stamping
AU - Boto, Fernando
AU - Lizuain, Zigor
AU - Cortadi, Alberto Jimenez
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Cold stamping
KW - Data mining
KW - Fault diagnosis
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85028642214&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67180-2_15
DO - 10.1007/978-3-319-67180-2_15
M3 - Conference contribution
AN - SCOPUS:85028642214
SN - 9783319671796
T3 - Advances in Intelligent Systems and Computing
SP - 157
EP - 166
BT - International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings
A2 - Perez Garcia, Hilde
A2 - Alfonso-Cendon, Javier
A2 - Sanchez Gonzalez, Lidia
A2 - Corchado, Emilio
A2 - Quintian, Hector
PB - Springer Verlag
T2 - 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
Y2 - 6 September 2017 through 8 September 2017
ER -