TY  - CONF
N2  - Roller element bearing fault diagnosis is crucial in industry to maintain that the machine is in good condition so that there is no delay of work due to machine breakdown. This paper discusses the use of Extreme Learning Machine (ELM) algorithm to classify bearing faults. The performance of ELM is compared with Back Propagation (BP) algorithm. It was found that the results show that the ELM has smaller training error rate and testing error rate as compared to BP. ELM also required lesser time to train the neural network and at the same time, able to achieve higher accuracy than BP. Overall, the performance of ELM is encouraging. © 2017 IEEE.
N1  - cited By 3; Conference of 3rd IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2017 ; Conference Date: 19 September 2017 Through 21 September 2017; Conference Code:134001
TI  - Classification of bearing faults using extreme learning machine algorithm
SP  - 1
ID  - scholars8040
KW  - Algorithms; Backpropagation algorithms; Classification (of information); Fault detection; Knowledge acquisition; Manufacture; Robotics
KW  -  Bearing fault; Bearing fault diagnosis; Extreme learning machine; Machine breakdown; Testing errors; Training error rate
KW  -  Learning algorithms
AV  - none
A1  - Teh, C.
A1  - Aziz, A.
A1  - Elamvazuthi, I.
A1  - Man, Z.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048407562&doi=10.1109%2fROMA.2017.8231823&partnerID=40&md5=b508537fd9f02ecfad3fbb7dd43e9113
EP  - 6
VL  - 2017-D
Y1  - 2017///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538625392
ER  -