A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments

Engineering Applications of Artificial Intelligence

Volumn 25, Issue 2, 2012, Pages 326-344

  Gryllias, K C ^a   Antoniadis, I A ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Condition monitoring; Fault detection; Rolling element bearings; Support Vector Machines; Vibration analysis

Indexed keywords

AUTOMATED DIAGNOSIS; BASIC CONCEPTS; CLASSIFICATION PROCEDURE; DATA PREPROCESSING; DEMODULATED SIGNALS; EXPERIMENTAL DATA; EXPERIMENTAL TEST; FREQUENCY DOMAINS; INDUSTRIAL ENVIRONMENTS; INDUSTRIAL TEST CASE; NORMAL CONDITION; ORDER ANALYSIS; PHYSICAL MODEL; RAW SIGNALS; ROLLING ELEMENT BEARING; ROLLING ELEMENT BEARINGS; ROTATING SPEED; ROTATION SPEED; SIMULATION DATA; SUDDEN CHANGE; SUPPORT VECTOR; SVM CLASSIFIERS; TEST CASE; TWO STAGE; TWO-STAGE RECOGNITION;

COMPUTER SIMULATION; CONDITION MONITORING; DYNAMIC RESPONSE; FAULT DETECTION; FEATURE EXTRACTION; FREQUENCY DOMAIN ANALYSIS; ROTATION; SIGNAL DETECTION; VIBRATION ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84855783889     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2011.09.010     Document Type: Article

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