Fault detection filter applied to structural health monitoring

Liberatore, Sauro; Speyer, Jason L.; Hsu, Andy Chunliang

doi:10.1109/cdc.2003.1272900

Cited by 4 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the next subsection, we derive necessary and sufficient conditions for solvability of the FDI problem for the RS system (15).…”

Section: A the Fdi Problem Statementmentioning

confidence: 99%

“…By augmenting the RS system (15) and the detection filter (17), one can obtain the representation ẋe (t) = A e x e (t) + B e u(t)…”

Section: B Necessary and Sufficient Conditionsmentioning

confidence: 99%

“…Consider the augmented system (18) and let N e =< ker C e |T A e >. Then, Q −1 N e is an unobservability subspace of the system (15), where Q is the embedding operator.…”

Section: B Necessary and Sufficient Conditionsmentioning

confidence: 99%

“…By using an array of sensors, the FDI problem of a beam structure has been investigated in [15]. In [9], by applying a finite difference method, a hyperbolic PDE is first approximated by a 2D Roesser model, and a geometric FDI approach is then developed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Invariant Subspaces of Riesz Spectral Systems with Application to Fault Detection and Isolation

Baniamerian¹,

Meskin²,

Khorasani³

2016

Preprint

View full text Add to dashboard Cite

A large class of hyperbolic and parabolic partial differential equation (PDE) systems, such as reaction-diffusion processes, when expressed in the infinite-dimensional (Inf-D) framework can be represented as Riesz spectral (RS) systems. Compared to the finite dimensional (Fin-D) systems, the geometric theory of Inf-D systems for addressing certain fundamental control problems, such as disturbance decoupling and fault detection and isolation (FDI), is rather quite limited due to complexity and existence of various types of invariant subspaces notions. Interestingly enough, these invariant concepts are equivalent for Fin-D systems, although they are different in Inf-D representation. In this work, first equivalence of various types of invariant subspaces that are defined for RS systems are investigated. This enables one to define and specify the unobservability subspace for RS systems. Specifically, necessary and sufficient conditions are derived for equivalence of various types of conditioned invariant subspaces. Moreover, by using duality properties, various controlled invariant subspaces are developed. It is then shown that finite-rankness of the output operator enables one to derive algorithms for computing invariant subspaces that under certain conditions, and unlike methods in the literature, converge in a finite number of steps. A geometric FDI methodology for RS systems is then developed by invoking the introduced invariant subspaces. Finally, necessary and sufficient conditions for solvability of the FDI problem are provided and analyzed.

show abstract

“…In the next subsection, we derive necessary and sufficient conditions for solvability of the FDI problem for the RS system (15).…”

Section: A the Fdi Problem Statementmentioning

confidence: 99%

“…By augmenting the RS system (15) and the detection filter (17), one can obtain the representation ẋe (t) = A e x e (t) + B e u(t)…”

Section: B Necessary and Sufficient Conditionsmentioning

confidence: 99%

“…Consider the augmented system (18) and let N e =< ker C e |T A e >. Then, Q −1 N e is an unobservability subspace of the system (15), where Q is the embedding operator.…”

Section: B Necessary and Sufficient Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Invariant Subspaces of Riesz Spectral Systems with Application to Fault Detection and Isolation

Baniamerian¹,

Meskin²,

Khorasani³

2016

Preprint

View full text Add to dashboard Cite

show abstract

“…With proper choice of filter feedback gains, these first-order model based observers predict outputs, which can identify and locate system component failures. Liberatore et al (2003) have used a fault detection filter for structural health monitoring, specifically for a simply supported beam; fault direction vectors obtained from predefined damage locations in the structure were used as the basis for identification for each of the possible fault locations. Kranock (2000) has used model based state space observers, as structural damage detection filters and parameterized the feedback gain for damage localization.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring using ARMarkov Observers

Dharap

Koh²,

Nagarajaiah

2006

Journal of Intelligent Material Systems and Structures

View full text Add to dashboard Cite

A new method based on a bank of ARMarkov observers is proposed in this study for determination of the extent of damage. The objective of this article is to present a new formulation using a predesigned set of ARMarkov observers to determine the extent of damage and track further changes in the stiffness of the damaged member. The primary advantages of the proposed formulation over the existing methods are: (1) ARMarkov observers are designed based on interaction matrix formulation so that knowledge about exact initial conditions is not necessary, and (2) noise statistics are not required a priori to design a bank of ARMarkov observers when compared to a bank of Kalman filters. The simulation results of several examples including a planar truss structure with progressive damage in a member are presented to highlight the capability of the proposed method. The proposed method works well in the case of either a full set or a limited number of available measurements. It is shown that sensitivity enhancing control (SEC) can be easily incorporated into the proposed method to enhance the sensitivity of structural damage. In case of noisy output measurements, it is shown that it is possible to distinguish between the errors due to structural damage and due to noise in output measurements.

show abstract