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Deep Variational Autoencoder: An Efficient Tool for PHM Frameworks

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Abstract

Deep learning (DL) has been recently used in several applications of machine health monitoring systems. Unfortunately , most of these DL models are considered as black-boxes with low interpretability. In this research, we propose an original PHM framework based on visual data analysis. The most suitable space dimension for the data visualization is the 2D-space, which necessarily involves a significant reduction from a high-dimensional to a low-dimensional data space. To perform the data analysis and the diagnostic interpretation in a PHM framework, a Variational Autoencoder (VAE) is used jointly with a classifier. The proposed model was evaluated to automatically recognize individual Partial Discharge (PD) sources for hydro generators monitoring.
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Dates and versions

hal-02868384 , version 1 (07-07-2020)

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Ryad Zemouri, Melanie Levesque, Normand Amyot, Claude Hudon, Olivier Kokoko. Deep Variational Autoencoder: An Efficient Tool for PHM Frameworks. 2020 Prognostics and Health Management Conference (PHM-Besançon), May 2020, Besancon, France. pp.235-240, ⟨10.1109/PHM-Besancon49106.2020.00046⟩. ⟨hal-02868384⟩
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