A State-Input Estimation Approach for Force Identification on an Automotive Suspension Component

Risaliti, Enrico; Cornelis, Bram; Tamarozzi, Tommaso; Desmet, Wim

doi:10.1007/978-3-319-29754-5_35

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…(18) respects the direct invertibility condition, which is not the case for the original state-space model given by Eq. (15). Furthermore, this is in fact exactly the same for the acceleration-based identification.…”

Section: Acceleration Datamentioning

confidence: 57%

“…Eqs. (15) and (18)], while σ 2 u is a tuning parameter, acting as a regularization parameter that limits the variation in the time history of the input. Its value significantly affects the quality of estimated solutions.…”

Section: Augmented Kalman Filtermentioning

confidence: 99%

“…In the second class of filters, the input-state estimation is performed simultaneously by considering an augmented state vector composed by both the state and input vectors [7]. In doing so, the resulting input-state estimation problem is solved from a conventional Kalman filter algorithm [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

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Practical issues on the applicability of Kalman filtering for reconstructing mechanical sources in structural dynamics

Aucejo

Smet

Deü

2019

Journal of Sound and Vibration

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Kalman-type filtering tends to become one of the favorite approaches for solving joint input-state estimation problems in the structural dynamics community. This article focuses on the applicability of the Augmented Kalman Filter (AKF) for reconstructing mechanical sources, addressing a set of practical issues that are frequently encountered in the engineering practice. In particular, this paper aims to help the reader to better apprehend some of the advantages and limitations of the application of the AKF in the context of purely input estimation problems. The present paper is not a simple collection of test cases, since it introduces a novel state-space representation of dynamical systems, based on the generalized-α method, as well as further insights in the tuning of Kalman filters from the Bayesian perspective. In this work, the various practical situations considered lead us to recommend to employ collocated acceleration measurements, when reconstructing excitation sources from the AKF. It is also demonstrated that the violation of some of the feasibility conditions proposed in the literature doesn't necessar

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Section: Acceleration Datamentioning

confidence: 57%

Section: Augmented Kalman Filtermentioning

confidence: 99%

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Practical issues on the applicability of Kalman filtering for reconstructing mechanical sources in structural dynamics

Aucejo

Smet

Deü

2019

Journal of Sound and Vibration

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“…In order to reduce the size of the FE model and thus the computational costs needed for computing the Kalman Filter equations, a Model Order Reduction (MOR) technique is used. As shown in [3,22,23], the following reduction basis is used:…”

Section: Introductionmentioning

confidence: 99%

“…A; B; C, and D are real matrices. The firsts two are functions of mass, stiffness and damping matrices of the reduced system [23], while C and D depend on the output response type y. The latter defines the measurement vector containing the observed quantities.…”

Section: Introductionmentioning

confidence: 99%

Kalman-based load identification and full-field estimation analysis on industrial test case

Cumbo

Tamarozzi

Janssens

et al. 2019

Mechanical Systems and Signal Processing

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The potential of the Augmented Kalman Filter algorithm is tested in this paper for joint state-input estimation in structural dynamics field. In view of inverse load identification, the filter is compared with the Transfer Path Analysis Matrix Inversion technique, commonly used for industrial applications. An existing Optimal Sensor Placement strategy for Kalman Filter is adopted and validated on real experimental data. The advantages of the proposed methods, through strain measurements information, are identified in the effort needed for data-acquisition and data-processing. The effectiveness of the filter and the quality of the results are demonstrated in this paper for an industrial test-case, such as a rear twistbeam suspension.

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A Method for Determining Impact Force for Single and Tri Axis Resonant Plate Shock Simulations

Ferri

Neil

2019

Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting &Amp; Dynamic Environments Testing, Volume 7

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A State-Input Estimation Approach for Force Identification on an Automotive Suspension Component

Cited by 8 publications

References 18 publications

Practical issues on the applicability of Kalman filtering for reconstructing mechanical sources in structural dynamics

Practical issues on the applicability of Kalman filtering for reconstructing mechanical sources in structural dynamics

Kalman-based load identification and full-field estimation analysis on industrial test case

A Method for Determining Impact Force for Single and Tri Axis Resonant Plate Shock Simulations

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