Fault tolerance analysis of sensor systems

Staroswiecki, Μ.; Hoblos, Ghaleb; Aitouche, A.

doi:10.1109/cdc.1999.827908

Cited by 19 publications

(9 citation statements)

References 7 publications

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“…In this case, Evensen recommends to use a pseudo inversion based on singular value decomposition [10]. Besides, the standard formulation of the Kalman gain can be recovered by linearizing the operator h which indirectly appears in relation (40). However, the advantages of this representation are that no linearization is required and there is no need to propagate the forecast error covariance matrix.…”

Section: General Framework and Notationsmentioning

confidence: 99%

“…Moreover, by using a single mobile sensor, the rank of the observation matrix also decreases when the distance between the sensor and the origin of the flux grows up. Some works on the network tolerance to loss of sensors, in the context of dynamic system surveillance, have allowed to analyze the observability in function of the set of sensors which are intact [40]. In the nonlinear case, the largest of the eigenvalues of the observability gramian, i.e.…”

Section: Sensor Network Deployment Strategy For Dual State-parameter mentioning

confidence: 99%

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Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

Roussel¹,

Bourgois

Benjelloun³

et al. 2013

Information Fusion

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Delmaire. Estimation of a semiphysical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: application to air field monitoring. Information Fusion, Elsevier, 2013, 14, pp.335-348. <10.1016/j.inffus.2013.03.001>. computation time was used by combining two filters consecutively activated in the same iteration. Finally, we proposed to complete the lack of spatial information of the sparse-observation network by adding a mobile sensor, which was routed to the location where the cell-by-cell output estimation error was the highest. Experimental results in the context of the standard lid-driven cavity problem revealed the presence of few zones of interest, where fixed sensors can be deployed to increase performances in terms of convergence speed and estimation quality. Finally, the study showed the feasibility of introducing some additional parameters which act as degrees of freedom, to perform large-eddy simulation of turbulent flows without numerical instabilities.

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Section: General Framework and Notationsmentioning

confidence: 99%

Section: Sensor Network Deployment Strategy For Dual State-parameter mentioning

confidence: 99%

Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

Roussel¹,

Bourgois

Benjelloun³

et al. 2013

Information Fusion

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show abstract

“…Two notions have been defined, namely the "principal redundancy of degree 1< and the "weak redundancy degree". Staroswiecki et al [9] have generalized these criteria by associating them with the observability of a linear functional. Two another notions has been defined, called the "strong redundancy degree" and "weak redundancy degree".…”

Section: Graph Representation and Fautlt Tolerance Evalutatingmentioning

confidence: 99%

An efficient algorithm for intelligent instrumentation system

Hoblos

Chafouk

Langlois

2004

2004 IEEE International Symposium on Industrial Electronics

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The reconfiguration of sensor svstems to solve estimation or control problem is one of the most important tasks in the design of process instrumentation. This paper proposes an algorithm, which guarantees the availability of all state variables even in the case of lost sensors. The v-ariable observabilitv is considered to be necessary for the process supervision of the svstem. Minimal and redundant sensor sets are organized into an oriented graph, which contains all the possible reconfiguration paths for which those variables remain observable. Based on redundancv degrees criteria, an optimal reconfiguration will be used to observe and reconstruct lost information. An example illustrates the proposed algorithm.

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“…A similar idea can also be seen in Frei, Kraus, and Blanke (1999). Staroswiecki, Hoblos, and Aitouche (1999) evaluate the size of the set of fault tolerant situations, namely the number of recoverable faults and the mean time until a non-recoverable one occurs. Staroswiecki (2002) analyses fault recoverability under possible energy limitation constraints.…”

Section: Introductionmentioning

confidence: 96%

Fault recoverability analysis of switched systems

Yang

Jiang

Staroswiecki

2012

International Journal of Systems Science

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This article provides an approach to evaluate the fault recoverability of switched systems (SS) under given energetic constraints. Such evaluation reveals the capability of the SS to tolerate the worst faults over a prescribed set under possible energy constraints. The evaluation is achieved by using the smallest eigenvalue of a proposed 'switched-system controllability Gramian'. Simulation results about a longitudinal flight control process example are presented to show the effectiveness of the proposed method.

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Fault tolerance analysis of sensor systems

Cited by 19 publications

References 7 publications

Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

An efficient algorithm for intelligent instrumentation system

Fault recoverability analysis of switched systems

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