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"
This project helps manufacturing engineers and maintenance professionals monitor the wear of their cutting tools in real-time. By analyzing sensor data from milling machines, it identifies unusual patterns that indicate a tool is becoming worn or failing. The output is an anomaly detection signal, helping predict when tool replacement or maintenance is needed, preventing unexpected breakdowns and improving production efficiency.
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Use this if you need to automatically detect the degradation or failure of milling tools based on sensor data to optimize maintenance schedules and minimize downtime.
Not ideal if you're looking for a general-purpose anomaly detection tool for non-industrial or non-sensor-based data.
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Jupyter Notebook
License
MIT
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Last pushed
Jun 01, 2021
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