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dc.contributor.authorMezair, Tinhinane
dc.contributor.authorDjenouri, Youcef
dc.contributor.authorBelhadi, Asma
dc.contributor.authorSrivastava, Gautam
dc.contributor.authorLin, Jerry Chun-Wei
dc.identifier.citationComputer Communications. 2022, 187, 164-171.en_US
dc.description.abstractAbstract The integration of 5G and Beyond 5G (B5G)/6G in Machine-to-Machine (M2M) communications, is making Industry 4.0 smarter. However, the goal of having a sustainable self-monitored industry has not been reached yet. State-of-the-art deep learning-based Fault Detection algorithms cannot handle heterogeneous data, meaning that more than one fault detection computational device has to be used for each data format, in addition to the inability to take advantage of the combination of all the information available in different formats to derive more accurate conclusions. Moreover, these algorithms rely on inefficient hyper-parameters tuning strategies. In this paper, we propose an Advanced Deep Learning framework for Fault Diagnosis in Industry 4.0 (ADL-FDI4), which combines Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN) and graph CNN (GNN), to handle heterogeneous data. Furthermore, our novel framework uses a Branch-and-Bound procedure to guide the learning process. Our experimental results show that ADL-FDI4 outperforms the state-of-the-art solutions in terms of detection rate and running time, and for that, it consumes less energy. In addition to handling heterogeneous data, which implies that one computational device is sufficient to handle all data formats.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectIndustry 4.0en_US
dc.subjectDeep Learningen_US
dc.subjectFault Detectionen_US
dc.subjectMachine to machine communicationen_US
dc.subjectSustainable 6Gen_US
dc.titleA sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environmentsen_US
dc.title.alternativeA sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environmentsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalComputer Communicationsen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal