Unsupervised feature selection based on fuzzy clustering for fault detection of the tennessee eastman process

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7637 LNAI, Issue , 2012, Pages 350-360

  Bedoya, C ^a   Uribe, C ^a   Isaza, C ^a  

^a UNIVERSIDAD DE ANTIOQUIA   (Colombia)

Author keywords

Fault detection; Feature selection; Fuzzy clustering; Tennessee Eastman process

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; FAULT DETECTION; FEATURE EXTRACTION;

AUTOMATIC IDENTIFICATION; AUTOMATIC MEASUREMENT SYSTEM; FAULT IDENTIFICATIONS; LARGE AMOUNTS OF DATA; LIKELIHOOD OF FAILURES; NUMBER OF COMPONENTS; TENNESSEE EASTMAN PROCESS; UNSUPERVISED FEATURE SELECTION;

FUZZY CLUSTERING;

EID: 84906682676     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34654-5_36     Document Type: Conference Paper

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