A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics
1
Center for Risk and Reliability, University of Maryland, College Park, MD 20742, USA
2
Department of Mechanical Engineering, University of Chile, Santiago 8320000, Chile
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 176; https://doi.org/10.3390/s20010176
Received: 12 November 2019
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Revised: 11 December 2019
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Accepted: 21 December 2019
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Published: 27 December 2019
(This article belongs to the Section Internet of Things)
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.
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Keywords:
generative adversarial networks; variational autoencoders; prognostics and health management; remaining useful life; multi-sensor fusion
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MDPI and ACS Style
Verstraete, 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. https://doi.org/10.3390/s20010176
AMA Style
Verstraete D, Droguett E, Modarres M. A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics. Sensors. 2020; 20(1):176. https://doi.org/10.3390/s20010176
Chicago/Turabian StyleVerstraete, David, Enrique Droguett, and Mohammad Modarres. 2020. "A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics" Sensors 20, no. 1: 176. https://doi.org/10.3390/s20010176
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