A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics
Author
dc.contributor.author
Verstraete, David
Author
dc.contributor.author
Droguett, Enrique
Author
dc.contributor.author
Modarres, Mohammad
Admission date
dc.date.accessioned
2020-05-13T22:44:05Z
Available date
dc.date.available
2020-05-13T22:44:05Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Sensors 2020, 20, 176
es_ES
Identifier
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10.3390/s20010176
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/174710
Abstract
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Multi-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.
es_ES
Patrocinador
dc.description.sponsorship
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT FONDECYT
1190720