A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

dc.contributor.authorVerstraete, David
dc.contributor.authorDroguett, Enrique
dc.contributor.authorModarres, Mohammad
dc.date.accessioned2023-11-13T18:14:05Z
dc.date.available2023-11-13T18:14:05Z
dc.date.issued2019-12-27
dc.description.abstractMulti-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
dc.description.urihttps://doi.org/10.3390/s20010176
dc.identifierhttps://doi.org/10.13016/dspace/v0fu-xjq6
dc.identifier.citationVerstraete, D.; Droguett, E.; Modarres, M. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics. Sensors 2020, 20, 176.
dc.identifier.urihttp://hdl.handle.net/1903/31368
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtA. James Clark School of Engineeringen_us
dc.relation.isAvailableAtMechanical Engineeringen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectgenerative adversarial networks
dc.subjectvariational autoencoders
dc.subjectprognostics and health management
dc.subjectremaining useful life
dc.subjectmulti-sensor fusion
dc.titleA Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics
dc.typeArticle
local.equitableAccessSubmissionNo

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