A Method for Multiple Fault Detection and Isolation of Redundant Inertial Navigation Sensor Configurations

2006 ◽  
Author(s):  
D. Skoogh ◽  
A. Lennartsson
Keyword(s):  
Fault Detection ◽  
Inertial Navigation ◽  
Fault Detection And Isolation ◽  
Multiple Fault

Performance Analysis of Fault Detection and Identification for Multiple Faults in GNSS and GNSS/INS Integration

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Muwaffaq Alqurashi ◽  
Jinling Wang
Keyword(s):  
Performance Analysis ◽  
Fault Detection ◽  
Real Time ◽  
Correlation Coefficients ◽  
Geometric Constraints ◽  
Fault Detection And Isolation ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification

AbstractFor positioning, navigation and timing (PNT) purposes, GNSS or GNSS/INS integration is utilised to provide real-time solutions. However, any potential sensor failures or faulty measurements due to malfunctions of sensor components or harsh operating environments may cause unsatisfactory estimation for PNT parameters. The inability for immediate detecting faulty measurements or sensor component failures will reduce the overall performance of the system. So, real time detection and identification of faulty measurements is required to make the system more accurate and reliable for different applications that need real time solutions such as real time mapping for safety or emergency purposes. Consequently, it is necessary to implement an online fault detection and isolation (FDI) algorithm which is a statistic-based approach to detect and identify multiple faults.However, further investigations on the performance of the FDI for multiple fault scenarios is still required. In this paper, the performance of the FDI method under multiple fault scenarios is evaluated, e.g., for two, three and four faults in the GNSS and GNSS/INS measurements under different conditions of visible satellites and satellites geometry. Besides, the reliability (e.g., MDB) and separability (correlation coefficients between faults detection statistics) measures are also investigated to measure the capability of the FDI method. A performance analysis of the FDI method is conducted under the geometric constraints, to show the importance of the FDI method in terms of fault detectability and separability for robust positioning and navigation for real time applications.

Robust multiple-fault detection and isolation: A gradient flow approach

2008 ◽  
Vol 22 (8) ◽  
pp. 739-756 ◽  
Author(s):  
Alessandro Casavola ◽  
Domenico Famularo ◽  
Giuseppe Franzè
Keyword(s):  
Fault Detection ◽  
Gradient Flow ◽  
Fault Detection And Isolation ◽  
Multiple Fault

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 A new scheme for multiple fault detection and isolation for rotational mechatronic systems through analytical redundancy and adaptive filtering

DYNA ◽  
2019 ◽  
Vol 86 (209) ◽  
pp. 40-48
Author(s):  
Edwin Villarreal López
Keyword(s):  
Fault Detection ◽  
Production Systems ◽  
Static Friction ◽  
Research Field ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Algorithm Optimization ◽  
Mechatronic Systems ◽  
Multiple Fault ◽  
Analytical Redundancy

Although Fault Detection and Isolation systems have been widely studied in recent years, it is still a very active research field due to its relevance in industrial production systems. In this paper, a new approach for multiple fault detection by using residual evaluation is proposed. First, an analytical redundancy scheme for residual generation is applied using nonlinear autoregressive networks with exogenousinputs for normal and faulty conditions. Simultaneous fault data is included in the training set in order to ensure multiple fault detection.Then, an adaptive filter considering statistic measures from input is used to increase sensibility and robustness. Filter coefficients are obtained off-line through genetic algorithm optimization. Finally, a neural network classifier is used for fault isolation. The proposed algorithm is tested on a rotary mechatronic test bench for backlash, bearing static friction and transmission faults to show the effectiveness of the proposed detection.

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