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Mathematical Problems in Engineering
Volume 2014, Article ID 824765, 7 pages
Research Article

A Novel Improved ELM Algorithm for a Real Industrial Application

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Received 2 December 2013; Accepted 29 January 2014; Published 16 April 2014

Academic Editor: Ramachandran Raja

Copyright © 2014 Hai-Gang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


It is well known that the feedforward neural networks meet numbers of difficulties in the applications because of its slow learning speed. The extreme learning machine (ELM) is a new single hidden layer feedforward neural network method aiming at improving the training speed. Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM. In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm. The proposed algorithm is employed in bearing fault detection using stator current monitoring. Simulative results show that R-ELM algorithm has better stability and generalization performance compared with the original ELM and the other neural network methods.