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Feature Extraction and Selection in Tool Condition Monitoring System

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AI 2002: Advances in Artificial Intelligence (AI 2002)

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Abstract

In order to predict tool state, this paper introduces the application of feature extraction and feature selection by automatic relevance determination (ARD) to explore the optimal feature set of AE signals in tool condition monitoring system(TCMS). The experiment results confirm that this selected AE feature set is more effective and efficient to recognize tool state over various cutting conditions.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, 119260, Singapore

    Sun Jie, G.S. Hong, M. Rahman & Y.S. Wong

Authors
  1. Sun Jie
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  2. G.S. Hong
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  3. M. Rahman
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  4. Y.S. Wong
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Editor information

Editors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, ACT 2600, Canberra, Australia

    Bob McKay

  2. Computer Science Laboratory, Australian National University, RSISE Building, ACT 0200, Canberra, Australia

    John Slaney

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© 2002 Springer-Verlag Berlin Heidelberg

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Jie, S., Hong, G., Rahman, M., Wong, Y. (2002). Feature Extraction and Selection in Tool Condition Monitoring System. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_43

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  • DOI: https://doi.org/10.1007/3-540-36187-1_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00197-3

  • Online ISBN: 978-3-540-36187-9

  • eBook Packages: Springer Book Archive

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