Cutting anomaly detection in end-milling by multimodal variational autoencoder

Cutting anomaly detection in end-milling by multimodal variational autoencoder

Anomaly detection for predictive maintenance in the cutting process is one of the challenging problems in shop-floor management. A modern machine learning approach, including deep learning, has been widely studied for the last decade. This study focuses on the multimodality of various cutting time-s...

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Bibliographic Details
Main Authors: Kazuya ODA, Haruhiko SUWA, Koji MURAKAMI
Format: Article
Language:Japanese
Published: The Japan Society of Mechanical Engineers 2023-01-01
Series:Nihon Kikai Gakkai ronbunshu
Subjects:
end-milling
anomaly detection
multimodal time series
unsupervised learning
variational autoencoder
generative model
Online Access:https://www.jstage.jst.go.jp/article/transjsme/89/918/89_22-00290/_pdf/-char/en

Internet

https://www.jstage.jst.go.jp/article/transjsme/89/918/89_22-00290/_pdf/-char/en

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