Data mining based full ceramic bearing fault diagnostic system using AE sensors

IEEE Transactions on Neural Networks

Volumn 22, Issue 12 PART 1, 2011, Pages 2022-2031

  He, David ^a   Li, Ruoyu ^a   Zhu, Junda ^a   Zade, Mikhail ^a  

^a University of Illinois at Chicago   (United States)

Author keywords

Data mining; fault diagnosis; full ceramic bearings

Indexed keywords

ACOUSTIC EMISSION SENSORS; AE SENSORS; BEARING DIAGNOSTICS; CERAMIC BEARING; CONDITION INDICATORS; FAULT CHARACTERISTICS; FAULT CLASSIFIER; FAULT DIAGNOSTIC SYSTEMS; FAULT FEATURE; FAULT TESTS; HILBERT HUANG TRANSFORMS; K NEAREST NEIGHBOR ALGORITHM; OUTER RACES; PROCESSING METHOD; STEEL COUNTERPART;

DATA MINING; ELECTRIC FAULT CURRENTS; FAILURE ANALYSIS; NONMETALLIC BEARINGS; PATTERN RECOGNITION; PROCESSING; SENSORS; SIGNAL PROCESSING;

CERAMIC MATERIALS;

ACOUSTICS; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CERAMICS; DATA MINING; EQUIPMENT; INSTRUMENTATION; METHODOLOGY; SOUND DETECTION; TRANSDUCER;

ACOUSTICS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CERAMICS; DATA MINING; EQUIPMENT FAILURE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SOUND SPECTROGRAPHY; TRANSDUCERS;

EID: 83855162819     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2169087     Document Type: Article

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