RT Journal Article
T1 A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0
A1 Navajas-Guerrero, Adriana
A1 Manjarres, Diana
A1 Portillo, Eva
A1 Landa-Torres, Itziar
AB In the era of technological advances and Industry 4.0, massive data collection and analysis is a common approach followed by many industries and companies worldwide. One of the most important uses of data mining and Machine Learning techniques is to predict possible breaks or failures in industrial processes or machinery. This research designs and develops a hyper-heuristic inspired methodology to autonomously identify significant parameters of the time series that characterize the behaviour of relevant process variables enabling the prediction of failures. The proposed hyper-heuristic inspired approach is based on the combination of an optimization process performed by a meta-heuristic algorithm (Harmony Search) and feature based statistical methods for anomaly detection. It demonstrates its adaptability to different failure cases without expert domain knowledge and the capability of autonomously identifying most relevant parameters of the time series to detect the abnormal behaviour prior to the final failure. The proposed solution is validated against a real database of a cold stamping process yielding satisfactory results respect to a novel AUC_ROC based metric, named AUC_MOD, and other conventional metrics, i.e., Specificity, Sensitivity and False Positive Rate.
SN 0360-8352
YR 2022
FD 2022-09
LK https://hdl.handle.net/11556/2959
UL https://hdl.handle.net/11556/2959
LA eng
NO Navajas-Guerrero , A , Manjarres , D , Portillo , E & Landa-Torres , I 2022 , ' A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0 ' , Computers and Industrial Engineering , vol. 171 , 108381 . https://doi.org/10.1016/j.cie.2022.108381
NO Publisher Copyright: © 2022 Elsevier Ltd
NO This research is part of the project 3KIA (KK-2020/00049), (partially) funded by the SPRI-Basque Government through the ELKARTEK program.
DS TECNALIA Publications
RD 31 jul 2024