Title :
Online fault detection of HRG based on an improved support vector machine
Author :
Zi-Yang Qi ; Qing-Hua Li ; Guo-xing Yi ; Yang-Guang Xie ; Hong-Tao Dang
Author_Institution :
Space control & inertial Technol. Res. center, Harbin Inst. of Technol., Harbin, China
Abstract :
An improved support vector machine (SVM) model is proposed to perform online fault detection of the navigation system with hemispherical resonator gyro (HRG). The proposed model is based on sliding window SVM prediction and least square (LS) method, which can satisfy the prediction demand of the HRG output characteristic of nonlinearity, non-determinism and randomness. The proposed model can overcome the explosion of calculation of traditional SVM method, and it also improves the prediction accuracy compared to the GM(1,1) model and BP neural network. Finally, simulations of HRG fault patterns are used to verify the correctness and effectiveness of the online fault detection model.
Keywords :
fault diagnosis; gyroscopes; mechanical engineering computing; navigation; random processes; support vector machines; HRG fault pattern simulation; HRG nondeterminism; HRG nonlinearity; HRG output characteristic; HRG randomness; LS method; hemispherical resonator gyro; improved support vector machine model; least square method; navigation system; online fault detection model; sliding window SVM prediction; HRG; Least square method; Moving window; Prediction model; SVM;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location :
Tianjin
DOI :
10.1109/ICMLC.2013.6890487