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Conference papers
Deep Variational Autoencoder: An Efficient Tool for PHM Frameworks
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.
Cited literature [17 references]
https://hal-cnam.archives-ouvertes.fr/hal-02868384
Contributor : Ryad Zemouri <>
Submitted on : Tuesday, July 7, 2020 - 11:30:30 AM
Last modification on : Friday, July 17, 2020 - 9:38:01 AM
Long-term archiving on: : Friday, November 27, 2020 - 1:05:17 PM
Contributor : Ryad Zemouri <>
Submitted on : Tuesday, July 7, 2020 - 11:30:30 AM
Last modification on : Friday, July 17, 2020 - 9:38:01 AM
Long-term archiving on: : Friday, November 27, 2020 - 1:05:17 PM
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Article_PHM_V5(1).pdf
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