Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"
deep-learning
recall
trend
beta-vae
advanced-manufacturing
precision
milling
anomaly-detection
machinery-condition-monitoring
masc-thesis
latent-spaces
tool-wear-monitoring
tool-condition-monitoring
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Updated
Jun 1, 2021 - Jupyter Notebook