The fault diagnosis approach for gears using multidimensional features and intelligent classifier

Noise and Vibration Worldwide

Volumn 41, Issue 10, 2010, Pages 76-86

  Li, Zhixiong ^a   Yan, Xinping ^a   Yuan, Chengqing ^a   Zhao, Jiangbin ^a   Peng, Zhongxiao ^b  

^a WUHAN UNIVERSITY OF TECHNOLOGY   (China)

^b JAMES COOK UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS RESULTS; AR PARAMETER; AUTO REGRESSIVE MODELS; AUTOMATIC RECOGNITION; DIAGNOSIS TECHNIQUES; EMPIRICAL MODE DECOMPOSITION; ENERGY DISTRIBUTIONS; FAULT CHARACTERISTICS; FAULT DIAGNOSIS; FEATURE SETS; FEATURE SPACE; GEAR FAILURE; GEAR FAULTS; GEAR MECHANISMS; GEAR VIBRATION; INCIPIENT FAULT DETECTION; INTELLIGENT CLASSIFIERS; INTELLIGENT DIAGNOSIS METHODS; INTRINSIC STRUCTURES; ISOMETRIC MAPPING; LOW-DIMENSIONAL SPACES; MULTI-FAULT DIAGNOSIS; MULTIPLE FAULTS; NONLINEAR DIMENSIONALITY REDUCTION; NORMAL OPERATIONS; OPERATION CONDITIONS; PROBABILISTIC NEURAL NETWORKS; SINGLE CRACK; SPEED-UPS; STATISTICAL FEATURES; VIBRATION SIGNAL; WAVELET PACKET TRANSFORMS; WIGNER-VILLE DISTRIBUTION;

ALGORITHMS; CLASSIFIERS; ELECTRIC FAULT LOCATION; INDUSTRIAL APPLICATIONS; MACHINERY; NEURAL NETWORKS; SIGNAL PROCESSING; WAVELET DECOMPOSITION;

FAULT DETECTION;

EID: 78650320165     PISSN: 09574565     EISSN: None     Source Type: Journal    
DOI: 10.1260/0957-4565.41.10.76     Document Type: Article

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