Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Neurocomputing

Volumn 99, Issue , 2013, Pages 399-410

  Liu, Zhiwen ^a   Cao, Hongrui ^a   Chen, Xuefeng ^a   He, Zhengjia ^a   Shen, Zhongjie ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Distance evaluation technique; Empirical model decomposition; Fault diagnosis; Particle swarm optimization; Rolling element bearings; Wavelet support vector machine

Indexed keywords

BEARING FAULT DIAGNOSIS; CLASSIFICATION MODELS; CLASSIFICATION PROCESS; DISTANCE EVALUATION TECHNIQUE; EMPIRICAL MODEL DECOMPOSITION; FAULT DETECTION AND CLASSIFICATION; FAULT PATTERNS; KERNEL FUNCTION; KERNEL METHODS; OPTIMAL PARAMETER; PSO ALGORITHMS; RELIABLE OPERATION; ROLLING ELEMENT BEARING; SALIENT FEATURES; UNKNOWN PARAMETERS; VIBRATION SIGNAL; WAVELET BASIS; WAVELET FRAME; WAVELET SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION); CONDITION MONITORING; FAILURE ANALYSIS; FAULT DETECTION; PARTICLE SWARM OPTIMIZATION (PSO); WAVELET DECOMPOSITION;

SUPPORT VECTOR MACHINES;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; EQUIPMENT DESIGN; INTERMETHOD COMPARISON; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RADIAL BASED FUNCTION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; VIBRATION; WAVELET ANALYSIS;

EID: 84867863746     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.019     Document Type: Article

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