Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141773
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dc.contributor.authorZhao, Ruien_US
dc.contributor.authorYan, Ruqiangen_US
dc.contributor.authorChen, Zhenghuaen_US
dc.contributor.authorMao, Kezhien_US
dc.contributor.authorWang, Pengen_US
dc.contributor.authorGao, Robert X.en_US
dc.date.accessioned2020-06-10T09:02:31Z-
dc.date.available2020-06-10T09:02:31Z-
dc.date.issued2018-
dc.identifier.citationZhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213-237. doi:10.1016/j.ymssp.2018.05.050en_US
dc.identifier.issn0888-3270en_US
dc.identifier.urihttps://hdl.handle.net/10356/141773-
dc.description.abstractSince 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In addition, an experimental study on the performances of these approaches has been conducted, in which the data and code have been online. Finally, some new trends of DL-based machine health monitoring methods are discussed.en_US
dc.language.isoenen_US
dc.relation.ispartofMechanical Systems and Signal Processingen_US
dc.rights© 2018 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDeep learning and its applications to machine health monitoringen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.ymssp.2018.05.050-
dc.identifier.scopus2-s2.0-85048280939-
dc.identifier.volume115en_US
dc.identifier.spage213en_US
dc.identifier.epage237en_US
dc.subject.keywordsDeep Learningen_US
dc.subject.keywordsMachine Health Monitoringen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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