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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 7939607, 10 pages
Research Article

Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

Mechanical Engineering College, 97 West Heping Road, Shijiazhuang, Hebei Province 050003, China

Received 15 March 2016; Revised 6 August 2016; Accepted 29 August 2016

Academic Editor: Yan-Jun Liu

Copyright © 2016 Fei Gao and Jiangang Lv. 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.


Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.