TY - GEN
T1 - Vibration analysis for the classification of damage motor PT Petrokimia Gresik using fast fourier transform and neural network
AU - Musthofa, Arif
AU - Asfani, Dimas Anton
AU - Negara, I. Made Yulistya
AU - Fahmi, Daniar
AU - Priatama, Nirma
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/20
Y1 - 2017/1/20
N2 - The electric motor is an important equipment to use in the industrialized world. Because its function is very crucial, so the damage of this electric motor will directly affect the production performance. In this research, the vibration data of electric motor at PT. Petrokimia Gresik has been classified based on physical damage which is using both of Fast Fourier Transform (FFT) and neural network methods. There are 5 types of conditions to classify the damage of electric motors using a neural network, namely normal condition, unbalance, miss-alignment, looseness, and antifriction. From the results obtained, the differences amplitude values from each condition. This level of accuracy of neural network method for detecting the damage motors in this study is 100% accurate.
AB - The electric motor is an important equipment to use in the industrialized world. Because its function is very crucial, so the damage of this electric motor will directly affect the production performance. In this research, the vibration data of electric motor at PT. Petrokimia Gresik has been classified based on physical damage which is using both of Fast Fourier Transform (FFT) and neural network methods. There are 5 types of conditions to classify the damage of electric motors using a neural network, namely normal condition, unbalance, miss-alignment, looseness, and antifriction. From the results obtained, the differences amplitude values from each condition. This level of accuracy of neural network method for detecting the damage motors in this study is 100% accurate.
KW - classification of damage motor
KW - fast fourier transform
KW - neural network
KW - vibration analysis
UR - http://www.scopus.com/inward/record.url?scp=85016809558&partnerID=8YFLogxK
U2 - 10.1109/ISITIA.2016.7828690
DO - 10.1109/ISITIA.2016.7828690
M3 - Conference contribution
AN - SCOPUS:85016809558
T3 - Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy
SP - 381
EP - 386
BT - Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016
Y2 - 28 July 2016 through 30 July 2016
ER -
