Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses

Mechanical Systems and Signal Processing

Volumn 25, Issue 5, 2011, Pages 1765-1772

  De Moura, E P ^a   Souto, C R ^b   Silva, A A ^b   Irmao M A S ^c  

^a FEDERAL UNIVERSITY OF CEARÁ   (Brazil)

^b FEDERAL UNIVERSITY OF CAMPINA GRANDE   (Brazil)

^c UNIVERSIDADE FEDERAL DO VALE DO SÃO FRANCISCO   (Brazil)

Author keywords

Bearing; Detrended fluctuation analysis; Fault diagnosis; Hurst analysis; Pattern recognition; Vibration analysis

Indexed keywords

BEARING; BEARING FAULT; BEARING FAULT DIAGNOSIS; DIFFERENT FREQUENCY; FAULT CLASS; FAULT DIAGNOSIS; FAULT RECOGNITION; FLUCTUATION ANALYSIS; HURST ANALYSIS; LOAD CONDITION; PATTERN RECOGNITION TECHNIQUES; PRINCIPAL COMPONENTS ANALYSIS; RESCALED RANGE ANALYSIS; ROLLING BEARINGS; VIBRATION SIGNAL;

BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); NETWORK PERFORMANCE; NEURAL NETWORKS; PATTERN RECOGNITION; SIGNAL PROCESSING; VIBRATION ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79953857397     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2010.11.021     Document Type: Article

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