Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine

Mechanical Systems and Signal Processing

Volumn 29, Issue , 2012, Pages 404-414

  Wang, Yujing ^a,b   Kang, Shouqiang ^b,c   Jiang, Yicheng ^a   Yang, Guangxue ^b   Song, Lixin ^b   Mikulovich, V I ^c  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c BELARUSIAN STATE UNIVERSITY   (Belarus)

Author keywords

Empirical mode decomposition; Fault diagnosis; Multi class support vector machine; Nonstationary signal; Rolling bearing

Indexed keywords

AUTO REGRESSIVE MODELS; EMPIRICAL MODE DECOMPOSITION; FEATURE VECTORS; INTELLIGENT DIAGNOSIS; INTELLIGENT METHOD; MODEL PARAMETERS; MULTI-CLASS SUPPORT VECTOR MACHINES; NONSTATIONARY SIGNALS; PERFORMANCE DEGRADATION; RECOGNITION RATES; ROLLING BEARING; ROLLING BEARING VIBRATION; ROLLING BEARINGS;

BEARINGS (MACHINE PARTS); DEGRADATION; FAILURE ANALYSIS; IMAGE RETRIEVAL; SIGNAL PROCESSING; SINGULAR VALUE DECOMPOSITION; SPHERES;

SUPPORT VECTOR MACHINES;

EID: 84859433868     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2011.11.015     Document Type: Conference Paper

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