Fault Detection and Isolation for Redundant Inertial Navigation System Based on Asymmetric Precision

2021 ◽  
Author(s):  
Xuan Xiao ◽  
Jianrui Lu
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Fault Detection And Isolation

A Study on Fault Detection Method of Redundant Inertial Navigation System on Micro AUV

2014 ◽  
Vol 709 ◽  
pp. 473-479 ◽  
Author(s):  
Kun Peng He ◽  
Yu Ping Shao ◽  
Lin Zhang ◽  
Shou Lei Hu ◽  
Yuan Li
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Inertial Navigation ◽  
Optimization Method ◽  
Fault Detection And Isolation ◽  
Measurement Unit ◽  
Support Vector ◽  
Ratio Test

In order to improve the precision and reliability of the autonomous underwater vehicle (AUV) inertial navigation system, a redundant inertial measurement unit (RIMU) based on micro electromechanical system (MEMS) inertial sensors has been designed, then use support vector machine theory (SVM),construct multi-fault classifier training and combine three-step search parameter optimization method,to achieve rapid, automatic fault detection and isolation (FDI). With Monte Carlo simulation and experimental analysis, SVM method has more obvious advantages than conventional Generalized Likelihood ratio Test (GLT) on false alarm rate, undetected rate and correct isolation rate for common fault sources of RIMU, and can detect and identify the type and number of failure more effectively on redundant systems, and provide a guarantee for fault sensors isolation.

scholarly journals An Integration Method of Inertial Navigation System and Three-Beam Lidar for the Precision Landing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyue Zhang ◽  
Pengbo Liu ◽  
Chunxi Zhang
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Integration Method ◽  
Inertial Navigation ◽  
Measurement Model ◽  
Reconstruction Method ◽  
Integrated Navigation ◽  
Fault Resistance ◽  
Vehicle Test

To ensure the high accuracy, independence, and reliability of the measurement system in the unmanned aerial vehicle (UAV) landing process, an integration method of inertial navigation system (INS) and the three-beam Lidar is proposed. The three beams of Lidar are, respectively, regarded as an independent sensor to integrate with INS according to the conception of multisensor fusion. Simultaneously, the fault-detection and reconstruction method is adopted to enhance the reliability and fault resistance. First the integration method is described. Then the strapdown inertial navigation system (SINS) error model is introduced and the measurement model of SINS/Lidar integrated navigation is deduced under Lidar reference coordinate. The fault-detection and reconstruction method is introduced. Finally, numerical simulation and vehicle test are carried out to demonstrate the validity and utility of the proposed method. The results indicate that the integration can obtain high precision navigation information and the system can effectively distinguish the faults and accomplish the reconstruction to guarantee the normal navigation when one or two beams of the Lidar malfunction.

scholarly journals Fault Detection Method of Laser Inertial Navigation System Based on the Overlapping Model

2011 ◽  
Vol 17 (11) ◽  
pp. 1106-1116 ◽  
Author(s):  
Cheon-Joong Kim ◽  
Ki-Jeong Yoo ◽  
Hyeon-Suk Kim ◽  
Joon Lyou
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Detection Method ◽  
Inertial Navigation

Fault detection method of laser inertial navigation system using dither motion of RLG

2016 ◽  
Author(s):  
Cheon-Joong Kim ◽  
Hae-Sung Yu ◽  
Ju-Hyun Oh ◽  
In-Seop Lee ◽  
Dong-hun Kim ◽  
...  
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Detection Method ◽  
Inertial Navigation

scholarly journals Research on Adaptive Multi-Source Information Fault-Tolerant Navigation Method Based on No-Reference System Diagnosis

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2911 ◽  
Author(s):  
Ling Zhang ◽  
Yuchen Cui ◽  
Zhi Xiong ◽  
Jianye Liu ◽  
Jizhou Lai ◽  
...  
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Reference System ◽  
Inertial Navigation System ◽  
Detection Method ◽  
Fault Tolerant ◽  
Inertial Navigation ◽  
External Interference ◽  
Free System ◽  
Navigation Method

In order to obtain accurate and optimized navigation sensor information, it is necessary to study information fusion and fault diagnosis with high reliability, high precision and high autonomy, and then to propose a rapid and accurate intelligent decision-making scheme based on multi-source and heterogeneous navigation information. In view of the existing fault-tolerant navigation federated filter structure, the method of assuming the reference system (inertial navigation system) to be fault-free and then diagnosing the measuring sensor fault is generally adopted. Considering that the structure of the filter can’t detect and isolate the faults of the inertial navigation system, the performance of the MEMS inertial navigation system declines due to complex environments resulting from vibrations and temperature changes; additionally, external interference may lead to the direct failure of the MEMS inertial device. Therefore, this paper studies a fault-tolerant navigation method based on a no-reference system. For the sensor sub-system of a custom micro air vehicle (MAV), a fault detection method based on a reference-free system is proposed. Based on the fault type analysis, some improvements have been made to the existing residual chi-square detection method, and an interactive residual fault detection method with distributed states is proposed. On this basis, aiming at the characteristics of a reference-free system, the weight distribution scheme of the reference system and the tested systems are studied, and a self-regulation filter fusion and fault detection method based on reference-free system is designed.

scholarly journals Fault Tolerant Control in Redundant Inertial Navigation System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoqiang Dai ◽  
Lin Zhao ◽  
Zhen Shi
Keyword(s):  
Navigation System ◽  
Control Algorithm ◽  
Inertial Navigation System ◽  
Control Method ◽  
Fault Tolerant ◽  
Inertial Navigation ◽  
Fault Tolerant Control ◽  
Fault Detection And Isolation ◽  
Feedback Gain ◽  
Noise Characteristics

Conventional fault detection and isolation technology cannot fully ensure system redundancy features when sensors experience drift in a redundant inertial navigation system. A new fault tolerant control method employs state estimation and state feedback techniques to compensate the sensor drift. However, the method is sensitive to measurement noise characteristics, and the performance of the method nearly depends on the feedback gain. This paper proposes an improved fault tolerant control algorithm, which employs an adaptive extended Kalman particle filter (AEKPF) to deal with unknown noise characteristics and model inaccuracies. In addition, a drift factor is introduced in the improved fault tolerant controlin order to reduce the dependence of compensation system on the feedback gain. Simulation results show that the improved fault tolerant control algorithm can effectively correct the faulty sensor even when the multiple erroneous sensors are producing faulty outputs simultaneously. Meanwhile, the AEKPF is able to solve the problem of unknown non-Gaussian noise characteristics. Moreover, the feedback gain is significantly improved by the drift factor.

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