A process monitoring method based on noisy independent component analysis

Neurocomputing

Volumn 127, Issue , 2014, Pages 231-246

  Cai, Lianfang ^a   Tian, Xuemin ^a   Chen, Sheng ^b,c  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Fault detection rate; Fault detection time; Fourth order cumulant; Independent component analysis; Kurtosis; Process monitoring

Indexed keywords

CONTINUOUS STIRRED TANK REACTOR; FAULT DETECTION RATE; FOURTH ORDER CUMULANT; INDEPENDENT COMPONENT ANALYSIS(ICA); INDEPENDENT COMPONENTS; JOINT DIAGONALIZATION; KURTOSIS; MONITORING METHODS; RECURSIVE ESTIMATE;

BLIND SOURCE SEPARATION; ESTIMATION; FAULT DETECTION; FEATURE EXTRACTION; HIGHER ORDER STATISTICS; MIXING; PROCESS CONTROL; PROCESS MONITORING; SPURIOUS SIGNAL NOISE; MONITORING;

INDEPENDENT COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; DEFAULT MODE NETWORK; INDEPENDENT COMPONENT ANALYSIS; KERNEL METHOD; NOISY INDEPENDENT COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS MONITORING; SIMULATION; STRUCTURAL EQUATION MODELING; COMPARATIVE STUDY; CONFIDENCE INTERVAL; CONTROLLED STUDY; ENTROPY; LEARNING ALGORITHM; REACTION TIME; SOLID PHASE EXTRACTION; SUPPORT VECTOR MACHINE;

EID: 84888433338     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.07.029     Document Type: Article

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