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Learning of specific process monitors in machine tool supervision

Authors
Journal
Annual Review in Automatic Programming
0066-4138
Publisher
Elsevier
Publication Date
Volume
19
Identifiers
DOI: 10.1016/0066-4138(94)90050-7
Keywords
  • Machine Tools
  • Monitoring
  • Learning Systems
  • Pattern Recognition
  • Artificial Intelligence
  • Classification
Disciplines
  • Computer Science

Abstract

Abstract This text describes our generic approaches to monitoring and prognostic, emphasizing the application of learning techniques, and focuses on model-free specific supervision entities that can be realized by a learning-from-examples method. All necessary tools for the generation of a supervised learning of a process situation classifier will be outlined. Statistical feature selection and inductive numerical learning constitute the basis for the proposed architecture. A particular supervised nonparametric learning method, developed in-house, the Q ∗ -algorithm will be presented. Practical experiments for the monitoring of a lathe are carried out.

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