Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery

Applied Soft Computing Journal

Volumn 46, Issue , 2016, Pages 459-468

  Dou, Dongyang ^a,b   Zhou, Shishuai ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b XUZHOU INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Fault diagnosis; PNN; Rotating machinery; Rule; SVM

Indexed keywords

BEARINGS (MACHINE PARTS); COMPUTER AIDED DIAGNOSIS; CONDITION MONITORING; DECISION TABLES; DEFECTS; FAILURE ANALYSIS; MACHINERY; NEAREST NEIGHBOR SEARCH; NEURAL NETWORKS; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PATTERN RECOGNITION; ROLLER BEARINGS; ROTATING MACHINERY; SUPPORT VECTOR MACHINES;

CLASSIFICATION METHODS; INTELLIGENT FAULT DIAGNOSIS; K NEAREST NEIGHBOR ALGORITHM; NUMERICAL VARIABLES; POTENTIAL PROBLEMS; PROBABILISTIC NEURAL NETWORKS; REAL-TIME APPLICATION; RULE;

FAULT DETECTION;

EID: 84971299226     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2016.05.015     Document Type: Article

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