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Journal of Control Science and Engineering
Volume 2017 (2017), Article ID 1982879, 14 pages
Research Article

WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor

1College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
2College of Aerospace Engineering, Civil Aviation University of China, Tianjin 300300, China
3Tianjin Binhai International Airport, Tianjin 300300, China

Correspondence should be addressed to Yigang Sun

Received 13 June 2017; Revised 8 September 2017; Accepted 13 September 2017; Published 26 November 2017

Academic Editor: Zhixing Cao

Copyright © 2017 Zhen Zhao 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.


In order to diagnose sensor fault of aeroengine more quickly and accurately, a double redundancy diagnosis approach based on Weighted Online Sequential Extreme Learning Machine (WOS-ELM) is proposed in this paper. WOS-ELM, which assigns different weights to old and new data, implements weighted dealing with the input data to get more precise training models. The proposed approach contains two series of diagnosis models, that is, spatial model and time model. The application of double redundancy based on spatial and time redundancy can in real time detect the hard fault and soft fault much earlier. The trouble-free or reconstructed time redundancy model can be utilized to update the training model and make it be consistent with the practical operation mode of the aeroengine. Simulation results illustrate the effectiveness and feasibility of the proposed method.