ml-tool-wear
PublicAnomaly 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"
advanced-manufacturinganomaly-detectionbeta-vaedeep-learninglatent-spacesmachinery-condition-monitoringmasc-thesismillingprecisionrecall
Creat:2020-08-21T22:14:11
Update:2025-03-22T19:48:08
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