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Hybrid Architecture of Deep Convolutional Variational Auto-encoder for Remaining useful Life Prediction

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Abstract

The remaining useful life prediction is a key element in decision-making and maintenance strategies development. Therefore, in practical situation, it is usually affected by uncertainty. The aim of this work is hence to propose a deep learning method which predicts when an in-service machine will fail to overcome the latter problem. It is based on deep convolutional variational autoencoder (CVAE). The proposed approach is validated using the C-MAPSS dataset of the aero-engine. The model’s classification performance has reached a superior accuracy compared to existing models and it is used for machine failure prediction in different time windows.
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hal-03945270 , version 1 (18-01-2023)

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Ryad Zemouri, Zeina Al Masry, Ikram Remadna, Sadek Labib Terrissa, Noureddine Zerhouni. Hybrid Architecture of Deep Convolutional Variational Auto-encoder for Remaining useful Life Prediction. e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15), ESREL2020-PSAM15 Organizers, Nov 2020, Venise, Italy. pp.3592-3598, ⟨10.3850/978-981-14-8593-0_4876-cd⟩. ⟨hal-03945270⟩
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