relation: https://khub.utp.edu.my/scholars/8040/
title: Classification of bearing faults using extreme learning machine algorithm
creator: Teh, C.
creator: Aziz, A.
creator: Elamvazuthi, I.
creator: Man, Z.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Teh, C. and Aziz, A. and Elamvazuthi, I. and Man, Z.  (2017) Classification of bearing faults using extreme learning machine algorithm.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048407562&doi=10.1109%2fROMA.2017.8231823&partnerID=40&md5=b508537fd9f02ecfad3fbb7dd43e9113
relation: 10.1109/ROMA.2017.8231823
identifier: 10.1109/ROMA.2017.8231823