Fault-tolerant relative navigation based on Kullback–Leibler divergence

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097912
Author(s):  
Jun Xiong ◽  
Joon Wayn Cheong ◽  
Zhi Xiong ◽  
Andrew G Dempster ◽  
Shiwei Tian ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Relative Navigation ◽  
Fusion Procedure ◽  
Leibler Divergence ◽  
F Distribution ◽  
Hybrid Distribution ◽  
Exclusion Method

A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2-based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F-distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2-based method.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Open-Switch Fault-Tolerant Control of Power Converters in a Grid-Connected Photovoltaic System

2016 ◽  
Vol 7 (4) ◽  
pp. 1294 ◽  
Author(s):  
Bilal Boudjellal ◽  
Tarak Benslimane
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Mean Value ◽  
Boost Converter ◽  
Photovoltaic System ◽  
Current Form ◽  
Fault Indicator ◽  
Open Switch

This paper presents the study of an open switch fault tolerant control of a grid-connected photovoltaic system. The studied system is based on the classical DC–DC boost converter and a bidirectional 6-pulse DC–AC converter. The objective is to provide an open-switch fault detection method and fault-tolerant control for both of boost converter and grid-side converter (GSC) in a grid-connected photovoltaic system. A fast fault detection method and a reliable fault-tolerant topology are required to ensure continuity of service, and achieve a faster corrective maintenance. In this work, the mean value of the error voltages is used as fault indicator for the GSC, while, for the boost converter the inductor current form is used as fault indicator. The fault-tolerant topology was achieved by adding one redundant switch to the boost converter, and by adding one redundant leg to the GSC. The results of the fault tolerant control are presented and discussed to validate the proposed approach under different scenarios and different solar irradiances.

Research on Fault Detection Method of Converters

2012 ◽  
Vol 229-231 ◽  
pp. 1150-1153
Author(s):  
Wen Zhong Ma ◽  
Ke Cheng Chen ◽  
Yang Shan ◽  
Yan Li Wang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Detection Method ◽  
Fault Tolerant ◽  
Experimental Results ◽  
The Neural Network ◽  
Operation Control ◽  
Fault Information

The influence of converter faults to the system is introduced and the fault detection method based on wavelet transform and neural network is proposed in this paper. The fault information can be decomposed by wavelet transform, then the fault eigenvectors can be extracted and put into neural network for training and testing. Finally the neural network outputs specific codes, and thus the fault location and fault components of converters are confirmed, which lays the foundation for the fault-tolerant operation control of converters. Simulation and experimental results show the correctness and effectiveness of the method.

Transducer Fault Detection of Hot Rolling Screwdown System Based on Sequential Probability Ratio Method

2012 ◽  
Vol 590 ◽  
pp. 313-319
Author(s):  
Bao Kui Li ◽  
Xiao Lan Yao ◽  
Qing He Wu
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Hot Rolling ◽  
Detection Method ◽  
Ratio Method ◽  
Probability Ratio ◽  
System A ◽  
Rolling Line ◽  
Sequential Probability ◽  
Improved Methods

To solve the fault detection problems of position transducers operating continuously under complex conditions of hot rolling line, and improve the reliability of the control system, a fault detection method based on Kalman filter and sequential probability ratio was presented. Through building the control model of the system, processing the output data of transducers with Kalman filter, and processing the residuals with sequential probability ratio and improved methods, the judgment criterion of transducer fault detection and the method of data reconstruction were given. The simulation results show that, the method can quickly detect position transducer faults, give the reasonable estimated state value, and improve the reliability of the system.

scholarly journals A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression

2016 ◽  
Vol 69 (4) ◽  
pp. 905-919 ◽  
Author(s):  
Yixian Zhu ◽  
Xianghong Cheng ◽  
Lei Wang
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Gaussian Process ◽  
Navigation System ◽  
Detection Method ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Chi Squared

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

Application of EKF for an Electro-Hydraulic Servo System Using Model-Based Approach

2013 ◽  
Vol 385-386 ◽  
pp. 609-613
Author(s):  
Lin Zheng ◽  
Liang Liang Wu ◽  
Xue Ming Gu ◽  
Z. Shi ◽  
Andrew Ball
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Fault Detection ◽  
Hydraulic System ◽  
Detection Method ◽  
Method Comparison ◽  
Estimation Accuracy ◽  
Model Based ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper works on extended Kalman filter (EKF) for model-based fault detection of an electro-hydraulic system to deal with stochastic behaviour during control. A mathematical model of an electro-hydraulic system is developed. Some faults are introduced to evaluate the EKF fault detection method. Comparison of the EKF estimation accuracy and a linearised model-based accuracy shows the advantage of the EKF.

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