TY - GEN
T1 - Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data
AU - Yilmaz, Malik S.
AU - Ayaz, Emine
PY - 2009
Y1 - 2009
N2 - In this study the features for bearing fault diagnosis is investigated based on the analysis of temperature, vibration and current measurements of a 3 phase, 4 poles, 5 HP induction motors which are chemically, thermally and electrically aged by artificial aging methods. Then three adaptive neuro-fuzzy inference systems which takes the temperature, current and vibration measurements as inputs and the condition of the motors as output are established, and the performances of these networks are compared.
AB - In this study the features for bearing fault diagnosis is investigated based on the analysis of temperature, vibration and current measurements of a 3 phase, 4 poles, 5 HP induction motors which are chemically, thermally and electrically aged by artificial aging methods. Then three adaptive neuro-fuzzy inference systems which takes the temperature, current and vibration measurements as inputs and the condition of the motors as output are established, and the performances of these networks are compared.
KW - ANFIS
KW - Feature extraction
KW - Induction motor
UR - https://www.scopus.com/pages/publications/70449625152
U2 - 10.1109/EURCON.2009.5167779
DO - 10.1109/EURCON.2009.5167779
M3 - Conference contribution
AN - SCOPUS:70449625152
SN - 9781424438617
T3 - IEEE EUROCON 2009, EUROCON 2009
SP - 1140
EP - 1145
BT - IEEE EUROCON 2009, EUROCON 2009
T2 - IEEE EUROCON 2009, EUROCON 2009
Y2 - 18 May 2009 through 23 May 2009
ER -