Multimode process monitoring with bayesian inference-based finite Gaussian mixture models

AIChE Journal

Volumn 54, Issue 7, 2008, Pages 1811-1829

  Yu, Jie ^a,c   Qin, S Joe ^b  

^a The University of Texas at Austin   (United States)

^b University of Southern California ^*  (United States)

^c SHELL GLOBAL SOLUTIONS INTERNATIONAL B V   (United States)

Author keywords

Bayesian inference; Fault detection; Finite Gaussian mixture model; Global probabilistic index; Mahalanobis distance; Multimode process monitoring; Tennessee Eastman chemical process

Indexed keywords

AMERICAN INSTITUTE OF CHEMICAL ENGINEERS (AICHE); BAYESIAN INFERENCES; COMPLEX INDUSTRIAL PROCESSES; CONTINUOUS STIRRED TANK (CST); EARLY DETECTION; FINITE GAUSSIAN MIXTURE (FGM); FINITE GAUSSIAN MIXTURE (FGM) MODEL; GAUSSIAN; GAUSSIAN COMPONENTS; MONITORING APPROACH; MONITORING TECHNIQUES; MULTI MODES; MULTIPLE COMPONENTS; MULTIVARIATE PROCESSES; OPERATING CONDITIONS; OPERATING DATA; OPERATING MODES; PARTIAL LEAST SQUARES (PLS 1); PCA METHOD; POSTERIOR PROBABILITIES; PRINCIPAL COMPONENT ANALYSIS (PCA); PROCESS DATA; PROCESS KNOWLEDGE; STATISTICAL DISTRIBUTIONS; TENNESSEE EASTMAN CHALLENGE; UNIMODAL;

BAYESIAN NETWORKS; BLIND SOURCE SEPARATION; COMMUNICATION CHANNELS (INFORMATION THEORY); CURVE FITTING; FAULT DETECTION; FINANCIAL DATA PROCESSING; INFERENCE ENGINES; LEAST SQUARES APPROXIMATIONS; LINEAR SYSTEMS; OBJECT RECOGNITION; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; PROBABILITY DISTRIBUTIONS; PROCESS CONTROL; PROCESS MONITORING; RISK ASSESSMENT; TRELLIS CODES;

PROCESS ENGINEERING;

EID: 47549099484     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.11515     Document Type: Article

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