A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis

Journal of Process Control

Volumn 22, Issue 4, 2012, Pages 778-788

  Yu, Jie ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Dynamic Gaussian mixture model; Dynamic mode shifts; Fault detection; Fault diagnosis; Non Gaussian process; Particle filter

Indexed keywords

BAYESIAN INFERENCE; CONVENTIONAL MONITORING; DYNAMIC MODE SHIFTS; DYNAMIC MODES; DYNAMIC OPERATIONS; DYNAMIC PRINCIPAL COMPONENT ANALYSIS; FAULT DETECTION AND DIAGNOSIS; GAUSSIAN MIXTURE MODEL; MIXTURE MODEL; MONITORING APPROACH; NON-GAUSSIAN PROCESS; PARTICLE FILTER; PROBABILITY INDEX; PROCESS FAULT DETECTION; PROCESS VARIABLES; RESAMPLING METHOD; TENNESSEE EASTMAN;

BAYESIAN NETWORKS; FAILURE ANALYSIS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INFERENCE ENGINES; NONLINEAR FILTERING; PRINCIPAL COMPONENT ANALYSIS; PROCESS MONITORING;

FAULT DETECTION;

EID: 84862799924     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2012.02.012     Document Type: Article

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