scholarly journals Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Hehong Zhang ◽  
Yunde Xie ◽  
Zhiqiang Long
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Control Variable ◽  
Suspension System ◽  
Acceleration Sensor ◽  
Maglev Train ◽  
Tracking Differentiator ◽  
Practical Usefulness ◽  
Simulation And Experiment

A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

scholarly journals Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1836
Author(s):  
Shi Liang ◽  
Jiewei Zeng
Keyword(s):  
Fault Detection ◽  
Correlation Analysis ◽  
Historical Data ◽  
Detection Method ◽  
Suspension System ◽  
Health Index ◽  
Maglev Train ◽  
Complex Operation ◽  
Operation Conditions ◽  
Abnormal State

During actual engineering, due to the influence of complex operation conditions, the data of complex systems are distinct, and the range of similarity differs under complex operation conditions. Simultaneously, the length of the data used to calculate the similarity will also impact the result of the fault detection. According to these, this paper proposes a fault detection method based on correlation analysis and improved similarity. In the first place, the complex operation conditions are divided into several simple operation conditions via the existing historical data. In the next place, the length of the data used to calculate the similarity is determined by correlation analysis. Then, an improved similarity calculation method is proposed to make the range of the similarity under multi-operation conditions identical. Finally, this method is applied to the suspension system of the maglev train. The experiment results indicate that the method proposed in this paper can not only detect the fault or abnormal state of the suspension system but also observe the health index (HI) changes of the system at distinct times under multi-operation conditions.

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.

scholarly journals Fault Detection Method for Suspension Systems of Maglev Train Based on Optimized Random Matrix Theory

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 187523-187530
Author(s):  
Hailin Hu ◽  
Fu Feng ◽  
Xu Zhou ◽  
Jiewei Zeng ◽  
Ping Wang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Matrix Theory ◽  
Detection Method ◽  
Maglev Train ◽  
Suspension Systems

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.

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.

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