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"

Date Created 2020-08-21 (4 years ago)

Commits 66 (last one 3 years ago)

Stargazers 67 (0 this week)

Watchers 3 (0 this week)

Forks 25

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RepositoryStats indexes 620,786 repositories, of these tvhahn/ml-tool-wear is ranked #398,193 (36th percentile) for total stargazers, and #434,970 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #10,210/18,544.

tvhahn/ml-tool-wear is also tagged with popular topics, for these it's ranked: deep-learning (#6,282/8727), anomaly-detection (#238/348)

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trend recall milling beta-vae precision masc-thesis deep-learning latent-spaces anomaly-detection tool-wear-monitoring advanced-manufacturing tool-condition-monitoring machinery-condition-monitoring

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updated: 2025-02-27 @ 12:12am, id: 289286204 / R_kgDOET4oPA