Online state and input force estimation for multibody models employing extended Kalman filtering

Naets, Frank; Pastorino, Roland; Cuadrado, Javier; Desmet, Wim

doi:10.1007/s11044-013-9381-8

Cited by 37 publications

(26 citation statements)

References 33 publications

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“…The force estimates obtained from GPLFM, AKF and AKFdm are very close to each other and seem to track the force history with a constant delay of around 2s. This delayed behavior can sometimes be expected in force estimation approach as reported in [51,52]. The delayed behavior can be removed by applying a Kalman smoother as shown in Figure 26b.…”

Section: Application: a 76-storey Asce Benchmark Buildingmentioning

confidence: 82%

A Gaussian process latent force model for joint input-state estimation in linear structural systems

Nayek

Chakraborty

Narasimhan

2019

Mechanical Systems and Signal Processing

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The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force models (GPLFMs) are hybrid models that combine differential equations representing a physical system with data-driven non-parametric Gaussian process models. In this work, the unknown input forces acting on a structure are modelled as Gaussian processes with some chosen covariance functions which are combined with the mechanistic differential equation representing the structure to construct a GPLFM. The GPLFM is then conveniently formulated as an augmented stochastic state-space model with additional states representing the latent force components, and the joint input and state inference of the resulting model is implemented using Kalman filter. The augmented state-space model of GPLFM is shown as a generalization of the class of input-augmented state-space models, is proven observable, and is robust compared to conventional augmented formulations in terms of numerical stability. The hyperparameters governing the covariance functions are estimated using maximum likelihood optimization based on the observed data, thus overcoming the need for manual tuning of the hyperparameters by trial-and-error. To assess the performance of the proposed GPLFM method, several cases of state and input estimation are demonstrated using numerical simulations on a 10-dof shear building and a 76-storey ASCE benchmark office tower. Results obtained indicate the superior performance of the proposed approach over conventional Kalman filter based approaches.

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Section: Application: a 76-storey Asce Benchmark Buildingmentioning

confidence: 82%

A Gaussian process latent force model for joint input-state estimation in linear structural systems

Nayek

Chakraborty

Narasimhan

2019

Mechanical Systems and Signal Processing

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“…This allows a simultaneous estimation of both the inputs and the parameters, such that no particular approximation needs to be assumed with respect to the relation between these variables. In the past this augmented approach has been adopted for either state/input [2,8] or state/parameter [4] estimation, and has only recently been exploited in a fully coupled sense [6]. This work focuses on exploiting physical models rather than data-driven models.…”

Section: Related Workmentioning

confidence: 99%

Improved human-computer interaction for mechanical systems design through augmented strain/stress visualisation

Cosco

Desmet

Naets

2017

IJIEI

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Strain/stress evaluation is a crucial operation performed during several stages in the typical mechanical product development/life cycle. However it is often difficult to obtain an appropriate estimation of the strain and stress distribution due to the difficult to model operational conditions, unknown input forces and/or parameters. This makes it particularly difficult for a designer to evaluate whether the final product under testing meets all the operational design specifications. This work presents an extended Kalman filtering approach to obtain accurate strain and stress estimates of a structure under operational loading. This information is exploited in an augmented reality application to visualize strains and corresponding stresses on a real component. This provides a very efficient human-computer interface to evaluate strains and stresses on a physical prototype. This approach is therefore very suitable to improve the design of components due to the good overview of the performance. In order to obtain an efficient formulation, the developed approach is based on the exploitation of reduced order mechanical models based on high fidelity finiteelement design models. By including a parameterization in the reduced model, the approach can be made robust with respect to unknown operational parameters, like boundary conditions. The obtained paradigm is validated on a flexible beam with unknown input forces and length. The proposed approach permits a more natural visualization and interpretation of operational conditions. Our results encourage the adoption of the proposed approach not only for design validation but also on-line monitoring of structural components, opening new possibility in the field of Augmented Reality for Maintenance.

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“…This allows a simultaneous estimation of both the inputs and the parameters, such that no particular approximation needs to be assumed with respect to the relation between these variables. In the past this augmented approach has been adopted for either state/input [2], [8] or state/parameter [4] estimation, but not in a fully coupled sense. This work focuses on exploiting physical models rather than data-driven models.…”

Section: State Of the Artmentioning

confidence: 99%

An extended Kalman filter approach for augmented strain/stress visualization in mechanical systems

Naets

Cosco

Desmet

2014

2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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This work presents an extended Kalman filtering approach to obtain accurate strain and stress estimates of a structure under operational loading. This information is exploited in an augmented reality application to visualize strains and corresponding stresses on a real component. A parametrized reduced physical model allows an efficient computation of the stresses in the Kalman filter. The model is parametrized in order to give good robustness to uncertain parameters, by estimating the parameters concurrently with the states. In order to allow unknown loading conditions, also the unknown input forces are estimated. This approach offers a very efficient and robust estimation approach. On the other side, using augmented reality as the visualization paradigm, offers two major benefits: visualizing operational strains and stresses field instead of discrete quantities; collocating the results on top of the real component under investigation. The obtained paradigm, validated with a demonstration case through an experimental validation on a beam, permits a more natural visualization and interpretation of operational conditions. Our results encourage the adoption of the proposed approach for on-line monitoring of structural components, opening new possibility in the field of Augmented Reality for Maintenance.

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Online state and input force estimation for multibody models employing extended Kalman filtering

Cited by 37 publications

References 33 publications

A Gaussian process latent force model for joint input-state estimation in linear structural systems

A Gaussian process latent force model for joint input-state estimation in linear structural systems

Improved human-computer interaction for mechanical systems design through augmented strain/stress visualisation

An extended Kalman filter approach for augmented strain/stress visualization in mechanical systems

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