Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis

Mechanical Systems and Signal Processing

Volumn 54, Issue , 2015, Pages 259-276

  Wang, Yi ^a   Xu, Guanghua ^a,b   Liang, Lin ^a   Jiang, Kuosheng ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Fault diagnosis; Manifold learning; Permutation entropy; Rolling element bearing; Wavelet packet transform

Indexed keywords

CLUSTERING ALGORITHMS; ENTROPY; FAILURE ANALYSIS; FAULT DETECTION; ROLLER BEARINGS; SIGNAL TO NOISE RATIO; WAVELET ANALYSIS; WAVELET TRANSFORMS;

HIGH DIMENSIONAL DATA; LOW SIGNAL-TO-NOISE RATIO; MANIFOLD LEARNING; MANIFOLD LEARNING ALGORITHM; PERMUTATION ENTROPY; ROLLING ELEMENT BEARING; TRANSIENT COMPONENTS; WAVELET PACKET TRANSFORMS;

LEARNING ALGORITHMS;

EID: 84916198296     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2014.09.002     Document Type: Article

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