Tool wear monitoring using acoustic emissions by dominant-feature identification

IEEE Transactions on Instrumentation and Measurement

Volumn 60, Issue 2, 2011, Pages 547-559

  Zhou, Jun Hong ^a,b   Pang, Chee Khiang ^c   Zhong, Zhao Wei ^a   Lewis, Frank L ^d  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

Auto Regressive Moving Average with eXogenous inputs (ARMAX) model; least squares error (LSE); principal component analysis (PCA); principal feature analysis (PFA); singular value decomposition (SVD); tool condition monitoring (TCM)

Indexed keywords

EXOGENOUS INPUT; FEATURE ANALYSIS; LEAST SQUARES ERROR (LSE); PRINCIPAL COMPONENT ANALYSIS (PCA); SINGULAR VALUE DECOMPOSITION (SVD); TOOL CONDITION MONITORING (TCM);

ACOUSTIC EMISSION TESTING; ACOUSTIC EMISSIONS; ALGORITHMS; BALL MILLING; CONDITION MONITORING; CUTTING; CUTTING TOOLS; DATA HANDLING; DYNAMIC RESPONSE; EQUIPMENT; FORECASTING; MACHINE TOOLS; MILLING (MACHINING); SENSORS; SIGNAL ENCODING; SIGNAL FILTERING AND PREDICTION; SIGNAL PROCESSING; SINGULAR VALUE DECOMPOSITION; WEAR OF MATERIALS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 78651282656     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2010.2050974     Document Type: Article

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