Fault detection for process monitoring using improved kernel principal component analysis

2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009

Volumn 2, Issue , 2009, Pages 334-338

  Xu, Jie ^a,b   Hu, Shousong ^a   Shen, Zhongyu ^b  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b NANJING NORMAL UNIVERSITY   (China)

Author keywords

Fault detection; Feature vector selection; Kernel principal component analysis; Wavelet analysis

Indexed keywords

CHEMICAL PROCESS; COMPUTATION COMPLEXITY; DATA SETS; FEATURE SPACE; FEATURE VECTOR SELECTION; FEATURE VECTORS; GEOMETRICAL CONSIDERATIONS; KERNEL PRINCIPAL COMPONENT ANALYSIS; SIMULATION RESULT; TENNESSEE EASTMAN PROCESS; WAVELET PACKET TRANSFORMS;

ARTIFICIAL INTELLIGENCE; CHEMICAL DETECTION; FAULT DETECTION; FEATURE EXTRACTION; PROCESS MONITORING; WAVELET ANALYSIS; WAVELET TRANSFORMS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77949283344     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AICI.2009.43     Document Type: Conference Paper

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