A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition

Engineering Applications of Artificial Intelligence

Volumn 26, Issue 1, 2013, Pages 456-466

  Yu, Jie ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Bayesian inference; Fault diagnosis; Gaussian mixture model; Multimode process; Multivariate contribution decomposition; Posterior probability

Indexed keywords

BAYESIAN INFERENCE; CHEMICAL PROCESS; FAULT DIAGNOSIS METHOD; GAUSSIAN CLUSTERS; GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURES; GAUSSIAN MODES; MAHALANOBIS DISTANCES; MULTIMODES; MULTIVARIATE GAUSSIAN DISTRIBUTIONS; NON-GAUSSIAN PROCESS; OPERATING CONDITION; OPERATING DATA; OPERATING MODES; POSTERIOR PROBABILITY; STATISTICAL CONFIDENCE; TENNESSEE EASTMAN PROCESS; WEIGHTING FACTORS;

FAILURE ANALYSIS; FAULT DETECTION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; INFERENCE ENGINES; PRINCIPAL COMPONENT ANALYSIS;

BAYESIAN NETWORKS;

EID: 84870052903     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2012.09.003     Document Type: Article

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