Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network

Computers and Industrial Engineering

Volumn 60, Issue 4, 2011, Pages 511-518

  Wang, Huaqing ^a   Chen, Peng ^b  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b MIE UNIVERSITY   (Japan)

Author keywords

Centrifugal blower; Fault diagnosis; Neural network; Possibility theory; Rolling element bearing

Indexed keywords

BEARING FAULT; CENTRIFUGAL BLOWER; CERTAINTY FACTORS; CHARACTERISTICS OF VIBRATIONS; FAULT DIAGNOSIS; FAULT TYPES; FREQUENCY DOMAINS; INTELLIGENT DIAGNOSIS METHODS; POSSIBILITY THEORY; ROLLING BEARINGS; ROLLING ELEMENT BEARING; SEQUENTIAL DIAGNOSIS; SYMPTOM PARAMETERS; VIBRATION SIGNAL;

BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); BLOWERS; CENTRIFUGATION; FREQUENCY DOMAIN ANALYSIS; FUZZY NEURAL NETWORKS;

ROLLING;

EID: 79952630284     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2010.12.004     Document Type: Article

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