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