Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Applied Soft Computing Journal

Volumn 34, Issue , 2015, Pages 402-425

  Yuwono, Mitchell ^a   Guo, Ying ^b   Wall, Josh ^b   Li, Jiaming ^b   West, Sam ^b   Platt, Glenn ^b   Su, Steven W ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b CSIRO   (Australia)

Author keywords

Consensus clustering; Ensemble Rapid Centroid Estimation (ERCE); Fault detection and diagnosis; Feature selection; Heating Ventilation and Air Conditioning (HVAC) system; Particle Swarm Optimization

Indexed keywords

AIR CONDITIONING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLIMATE CONTROL; CLUSTER ANALYSIS; FEATURE EXTRACTION; FREQUENCY ESTIMATION; HEAT PUMP SYSTEMS; HIDDEN MARKOV MODELS; MARKOV PROCESSES; PARTICLE SWARM OPTIMIZATION (PSO); REDUNDANCY; SIGNAL DETECTION; TIME DELAY; VENTILATION;

CENTROID ESTIMATION; CONSENSUS CLUSTERING; FAULT DETECTION AND DIAGNOSIS; FEATURE SELECTION ALGORITHM; GENERALIZATION CAPABILITY; HEATING VENTILATION AND AIR CONDITIONING; NORMALIZED MUTUAL INFORMATION; UNSUPERVISED FEATURE SELECTION;

FAULT DETECTION;

EID: 84930627756     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.05.030     Document Type: Article

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