scholarly journals Research on Fault Detection and Isolation Algorithm for Hexacopters Based on Detection Filter

2016 ◽  
Author(s):  
Yumin Zhang ◽  
Hongdi Zhang ◽  
Junwu Deng
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Detection Filter

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.

Full- and Reduced-Order Fault Detection Filter Design With Application in Flow Transmission Lines

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Saeed Salavati ◽  
Karolos Grigoriadis ◽  
Matthew Franchek ◽  
Reza Tafreshi
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Filter Design ◽  
Sufficient Conditions ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Reduced Order ◽  
Fault Detection Filter ◽  
Order Filter ◽  
Detection Filter

The full- and reduced-order fault detection filter design is examined for fault diagnosis in linear time-invariant (LTI) systems in the presence of noise and disturbances. The fault detection filter design problem is formulated as an H∞ problem using a linear fractional transformation (LFT) framework and the solution is based on the bounded real lemma (BRL). Necessary and sufficient conditions for the existence of the fault detection filter are presented in the form of linear matrix inequalities (LMIs) resulting in a convex problem for the full-order filter design and a rank-constrained nonconvex problem for the reduced-order filter design. By minimizing the sensitivity of the filter residuals to noise and disturbances, the fault detection objective is fulfilled. A reference model can be incorporated in the design in order to shape the desired performance of the fault detection filter. The proposed fault detection and isolation (FDI) framework is applied to detect instrumentation and sensor faults in fluid transmission and pipeline systems. To this end, a lumped parameter framework for modeling infinite-dimensional fluid transient systems is utilized and a low-order model is obtained to pursue the instrumentation fault diagnosis objective. Full- and reduced-order filters are designed for sensor FDI. Simulations are conducted to assess the effectiveness of the proposed fault detection approach.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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