Publication
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
| dc.contributor.author | Fernandes, Marta | |
| dc.contributor.author | Corchado, Juan Manuel | |
| dc.contributor.author | Marreiros, Goreti | |
| dc.date.accessioned | 2023-02-01T09:44:56Z | |
| dc.date.embargo | 2035-12-31 | |
| dc.date.issued | 2022 | |
| dc.description.abstract | When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios. | pt_PT |
| dc.description.sponsorship | The present work has been developed under project PIANiSM (EUREKA - ITEA3: 17008; ANI|P2020 40125) and has received Portuguese National Funds through FCT (Portuguese Foundation for Science and Technology) under project UIDB/00760/2020 and Ph.D. Scholarship SFRH/BD/136253/2018. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1007/s10489-022-03344-3 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/22043 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation | EUREKA - ITEA3: 17008 | pt_PT |
| dc.relation | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| dc.relation | Data Stream Learning for Predictive Maintenance | |
| dc.relation.publisherversion | https://link.springer.com/article/10.1007/s10489-022-03344-3 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Fault detection | pt_PT |
| dc.subject | Fault prognosis | pt_PT |
| dc.subject | Predictive maintenance | pt_PT |
| dc.subject | Manufacturing industry | pt_PT |
| dc.subject | Industrial case-study | pt_PT |
| dc.title | Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| oaire.awardTitle | Data Stream Learning for Predictive Maintenance | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/POR_NORTE/SFRH%2FBD%2F136253%2F2018/PT | |
| oaire.citation.endPage | 14280 | pt_PT |
| oaire.citation.issue | 12 | pt_PT |
| oaire.citation.startPage | 14246 | pt_PT |
| oaire.citation.title | Applied Intelligence | pt_PT |
| oaire.citation.volume | 52 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | POR_NORTE | |
| person.familyName | Marreiros | |
| person.givenName | Goreti | |
| person.identifier.ciencia-id | A412-138E-2389 | |
| person.identifier.orcid | 0000-0003-4417-8401 | |
| person.identifier.rid | M-4583-2013 | |
| person.identifier.scopus-author-id | 9332465700 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | article | pt_PT |
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