A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0
No Thumbnail Available
Identifiers
Publication date
2022-09
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Export
Abstract
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.
Description
Publisher Copyright: © 2022 Elsevier Ltd
Type
Citation
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