A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower

Expert Systems with Applications

Volumn 38, Issue 1, 2011, Pages 545-552

  Wu, Jian Da ^a   Liao, Shu Yi ^a  

^a NATIONAL CHANGHUA UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

Empirical mode decomposition; Fault diagnosis; Feature extraction; Probabilistic neural networks

Indexed keywords

AIR CONDITIONERS; ARTIFICIAL NEURAL NETWORK; BACK-PROPAGATION NEURAL NETWORKS; CLASSIFICATION PERFORMANCE; COMPLEX SIGNAL; DATA ANALYSIS; DATA ANALYSIS TECHNIQUES; DIAGNOSIS SYSTEMS; EMD METHOD; EMPIRICAL MODE DECOMPOSITION; ENERGY FEATURE; FAULT CLASSIFICATION; FAULT CONDITIONS; FAULT DIAGNOSIS; FAULT DIAGNOSIS SYSTEMS; INTRINSIC MODE FUNCTIONS; NOISE EMISSIONS; NON-LINEAR; NONSTATIONARY; PROBABILISTIC NEURAL NETWORKS; SELF-ADAPTIVE; TRADITIONAL TECHNIQUES;

ACOUSTIC SIGNAL PROCESSING; AIR CONDITIONING; BLOWERS; DATA REDUCTION; FEATURE EXTRACTION; POWER QUALITY; TIME SERIES;

NEURAL NETWORKS;

EID: 77956616482     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.06.100     Document Type: Article

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