tvhahn/ml-tool-wear

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"

语言：Jupyter Notebook · GitHub 星标：80 · Fork：27

话题：advanced-manufacturing · anomaly-detection · beta-vae · deep-learning · latent-spaces · machinery-condition-monitoring · masc-thesis · milling

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