Early fault diagnosis of rotating machinery based on wavelet packets - Empirical mode decomposition feature extraction and neural network

Mechanical Systems and Signal Processing

Volumn 27, Issue 1, 2012, Pages 696-711

  Bin, G F ^a,b   Gao, J J ^a   Li, X J ^b   Dhillon, B S ^c  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b HUNAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Early fault diagnosis; EMD; Energy moment; Feature extraction; Neural network; WPD

Indexed keywords

BP NEURAL NETWORK MODEL; BP NEURAL NETWORKS; CHARACTERISTIC PARAMETER; DE-NOISING; EARLY FAULT; EMD; EMD METHOD; EMPIRICAL MODE DECOMPOSITION; ENERGY MOMENT; ENERGY OF VIBRATION; FAILURE FEATURES; FAULT DIAGNOSIS TECHNOLOGY; FAULT FEATURE; FAULT FREQUENCY; FAULT IDENTIFICATIONS; FAULT PATTERNS; FREQUENCY BAND WIDTH; INTRINSIC MODE FUNCTIONS; NARROW BANDWIDTH; ROTOR FAULT; SIGNAL FEATURES; SPECTRAL BANDWIDTH; THREE-LAYER; WAVELET PACKET; WAVELET PACKET DECOMPOSITIONS; WPD;

BANDWIDTH; ELECTRIC FAULT CURRENTS; FAULT DETECTION; FEATURE EXTRACTION; NETWORK LAYERS; ROTATING MACHINERY; ROTATION; WAVELET ANALYSIS; WAVELET DECOMPOSITION;

NEURAL NETWORKS;

EID: 82255174981     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2011.08.002     Document Type: Article

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