Feature extraction and selection in tool condition monitoring system

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

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

24 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAI 2002
Subtitle of host publicationAdvances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings
EditorsBob McKay, John Slaney
PublisherSpringer Verlag
Pages487-497
Number of pages11
ISBN (Print)3540001972, 9783540001973
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event15th Australian Joint Conference on Artificial Intelligence, AI 2002 - Canberra, Australia
Duration: 2 Dec 20026 Dec 2002

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2557
ISSN (Print)0302-9743

Conference

Conference15th Australian Joint Conference on Artificial Intelligence, AI 2002
Country/TerritoryAustralia
CityCanberra
Period2/12/026/12/02

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