Fault detection and diagnosis in process data using one-class support vector machines

Journal of Process Control

Volumn 19, Issue 10, 2009, Pages 1627-1639

  Mahadevan, Sankar ^a   Shah, Sirish L ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Fault detection; Fault diagnosis; Support vector machines

Indexed keywords

1-CLASS SVM; APPROPRIATE DISTANCES; CONVENTIONAL TECHNIQUES; DATA SETS; DETECTION AND DIAGNOSIS; DETECTION LATENCY; DISTANCE METRICS; ETCH PROCESS; FALSE ALARM RATE; FAULT DETECTION AND DIAGNOSIS; FAULT DETECTION RATE; FAULT DIAGNOSIS; FEATURE SELECTION METHODS; FEATURE SPACE; IN-PROCESS; NEW APPROACHES; NON-LINEAR; ONE-CLASS SUPPORT VECTOR MACHINE; PERFORMANCE MEASURE; PRINCIPAL COMPONENTS ANALYSIS; RECURSIVE FEATURE ELIMINATION; SVM-RFE; TENNESSEE EASTMAN;

ALGORITHMS; FAILURE ANALYSIS; NANOELECTRONICS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

FAULT DETECTION;

EID: 72149111135     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2009.07.011     Document Type: Article

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