A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0

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dc.contributor.authorNavajas-Guerrero, Adriana
dc.contributor.authorManjarres, Diana
dc.contributor.authorPortillo, Eva
dc.contributor.authorLanda-Torres, Itziar
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:59:35Z
dc.date.available2024-07-24T11:59:35Z
dc.date.issued2022-09
dc.descriptionPublisher Copyright: © 2022 Elsevier Ltd
dc.description.abstractIn 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.en
dc.description.sponsorshipThis research is part of the project 3KIA (KK-2020/00049), (partially) funded by the SPRI-Basque Government through the ELKARTEK program.
dc.description.statusPeer reviewed
dc.identifier.citationNavajas-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
dc.identifier.doi10.1016/j.cie.2022.108381
dc.identifier.issn0360-8352
dc.identifier.urihttps://hdl.handle.net/11556/2959
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85133446474&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofComputers and Industrial Engineering
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsCold stamping process
dc.subject.keywordsCollective Anomaly detection
dc.subject.keywordsFault detection
dc.subject.keywordsFault prediction
dc.subject.keywordsMultiple parameter optimization
dc.subject.keywordsTime series analysis
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsGeneral Engineering
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.titleA hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0en
dc.typejournal article
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