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.