Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification

IEEE Transactions on Industrial Informatics

Volumn 5, Issue 4, 2009, Pages 454-464

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

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

^c UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^d NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Least square error (LSE); Principal component analysis (PCA); Principal feature analysis (PFA); Recursive least squares (RLS); Singular value decomposition (SVD)

Indexed keywords

LEAST SQUARE ERROR (LSE); PRINCIPAL COMPONENT ANALYSIS (PCA); PRINCIPAL FEATURE ANALYSIS (PFA); RECURSIVE LEAST SQUARES (RLS); SINGULAR VALUE DECOMPOSITION (SVD);

COST REDUCTION; CUTTING TOOLS; DIAGNOSIS; FEATURE EXTRACTION; METAL WORKING TOOLS; MILLING (MACHINING); PRINCIPAL COMPONENT ANALYSIS; RECURSIVE FUNCTIONS; SECURITY OF DATA; SENSORS; SIGNAL PROCESSING; TIME MEASUREMENT; TIME SERIES; WEAR OF MATERIALS;

SINGULAR VALUE DECOMPOSITION;

EID: 70449517374     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2009.2023318     Document Type: Article

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