Fault diagnosis of rolling element bearing using time-domain features and neural networks

IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, ICIIS 2008

Volumn , Issue , 2008, Pages

  Sreejith, B ^a   Verma, A K ^a   Srividya, A ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Automated diagnosis; Bearing vibration; Log likelihood value; Time domain feature

Indexed keywords

AUTOMATED DIAGNOSIS; BEARING VIBRATION; FAULT DETECTION AND DIAGNOSIS; FAULT DIAGNOSIS; FEED-FORWARD NEURAL NETWORKS; INPUT FEATURES; LOCALIZED FAULTS; LOG-LIKELIHOOD VALUE; MECHANICAL COMPONENTS; MONITORING TECHNIQUES; ROLLING ELEMENT BEARINGS; TIME DOMAIN FEATURE; TRAINED NEURAL NETWORKS; VIBRATION DATUM; VIBRATION MONITORING; VIBRATION SIGNALS;

AUTOMATION; BEARINGS (STRUCTURAL); DAMAGE DETECTION; FAILURE ANALYSIS; FAULT DETECTION; INFORMATION SYSTEMS; MACHINERY; QUALITY ASSURANCE; ROLLING; TIME DOMAIN ANALYSIS;

NEURAL NETWORKS;

EID: 64949087144     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIINFS.2008.4798444     Document Type: Conference Paper

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