Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Effective fault diagnosis based on strong tracking UKF

Effective fault diagnosis based on strong tracking UKF Purpose – The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault‐tolerant re‐entry navigation requirements of reusable boost vehicle (RBV). Design/methodology/approach – A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ 2 ‐test‐based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors. Findings – The comparisons with χ 2 ‐test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method. Practical implications – This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles. Originality/value – A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology Emerald Publishing

Effective fault diagnosis based on strong tracking UKF

Loading next page...
 
/lp/emerald-publishing/effective-fault-diagnosis-based-on-strong-tracking-ukf-OHc05mN04M
Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0002-2667
DOI
10.1108/00022661111159889
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault‐tolerant re‐entry navigation requirements of reusable boost vehicle (RBV). Design/methodology/approach – A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ 2 ‐test‐based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors. Findings – The comparisons with χ 2 ‐test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method. Practical implications – This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles. Originality/value – A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV.

Journal

Aircraft Engineering and Aerospace TechnologyEmerald Publishing

Published: Sep 6, 2011

Keywords: Strong tracking; Unscented Kalman filter; Fault diagnosis; Chi‐square test; Reusable boost vehicle; Navigation; Rocket engines

References