Open AccessSelf-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder
Author(s) -
Tim Hahn,
Chris K. Mechefske
Publication year - 2021
Publication title -
international journal of hydromechatronics
Language(s) - English
Resource type - Journals
eISSN - 2515-0472
pISSN - 2515-0464
DOI - 10.1504/ijhm.2021.10035377
Subject(s) - autoencoder , artificial intelligence , feature engineering , deep learning , context (archaeology) , computer science , machine learning , convolutional neural network , f1 score , anomaly detection , artificial neural network , pattern recognition (psychology) , geology , paleontology