WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor
Joint Authors
Zhao, Zhen
Liu, Zhexu
Sun, Yigang
Liu, Jingya
Source
Journal of Control Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-26
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Zhao, Zhen& Liu, Zhexu& Sun, Yigang& Liu, Jingya. 2017. WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor. Journal of Control Science and Engineering،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1173405
Modern Language Association (MLA)
Zhao, Zhen…[et al.]. WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor. Journal of Control Science and Engineering No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1173405
American Medical Association (AMA)
Zhao, Zhen& Liu, Zhexu& Sun, Yigang& Liu, Jingya. WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor. Journal of Control Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1173405
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173405