Restricted diagonal detection filter and updating strategy for multiple fault detection and isolation

2010 ◽  
Vol 25 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Jean-Yves Keller ◽  
Dominique Sauter
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Multiple Fault ◽  
Detection Filter

Robust multiple-fault detection filter

2002 ◽  
Vol 12 (8) ◽  
pp. 675-696 ◽  
Author(s):  
Robert H. Chen ◽  
Jason L. Speyer
Keyword(s):  
Fault Detection ◽  
Multiple Fault ◽  
Fault Detection Filter ◽  
Detection Filter

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.

scholarly journals Robust FDI for a Class of Nonlinear Networked Systems with ROQs

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
An-quan Sun ◽  
Lin Zhang ◽  
Wen-feng Wang ◽  
Jun Wang ◽  
Tian-lin Niu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Sufficient Conditions ◽  
Networked Systems ◽  
Fault Detection And Isolation ◽  
Parameter Uncertainties ◽  
Fault Detection Filter ◽  
Mean Square Stability ◽  
The Difference ◽  
Detection Filter

This paper considers the robust fault detection and isolation (FDI) problem for a class of nonlinear networked systems (NSs) with randomly occurring quantisations (ROQs). After vector augmentation, Lyapunov function is introduced to ensure the asymptotically mean-square stability of fault detection system. By transforming the quantisation effects into sector-bounded parameter uncertainties, sufficient conditions ensuring the existence of fault detection filter are proposed, which can reduce the difference between output residuals and fault signals as small as possible underH∞framework. Finally, an example linearized from a vehicle system is introduced to show the efficiency of the proposed fault detection filter.

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
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