A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approa...

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Bibliographic Details
Main Authors: Maryam Assafo, Peter Langendorfer
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Anomaly detection
condition monitoring
feature selection
one-class classification
predictive maintenance
self-organizing feature maps
Online Access:https://ieeexplore.ieee.org/document/9462147/

Internet

https://ieeexplore.ieee.org/document/9462147/

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