Book Chapter10.1007/978-3-319-29754-5_35
A State-Input Estimation Approach for Force Identification on an Automotive Suspension Component
Enrico Risaliti,Enrico Risaliti,Bram Cornelis,Tommaso Tamarozzi,Tommaso Tamarozzi,Wim Desmet +5 more
- 01 Jan 2016
- pp 359-369
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TL;DR: In this paper, a coupled state-input estimation approach is used in order to combine experimental and simulation data, and a Kalman filter is then used to perform the estimation, where the system-underinvestigation is an automotive suspension component.
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Abstract: Input force evaluation is always a crucial step for the adequate design of any kind of mechanical system. Direct measurements of input forces typically involve devices that are expensive, intrusive or difficult to calibrate. This is also the case for Road Load Data Acquisition (RLDA) testing campaigns, where the loads due to the road excitations acting on a vehicle are acquired. During RLDA testing campaigns, expensive measurement wheels are commonly used. An appealing alternative procedure, which consists of the use of less expensive sensors in combination with a numerical model of the system, is investigated. In order to combine experimental and simulation data, a coupled state-input estimation approach is used in the proposed procedure. In this approach a finite element model of the system provides simulated data, while common accelerometers and strain gauges provide experimental data. A Kalman filter is then used in order to perform the estimation. This paper presents the derivation of the filter equations that are necessary for the envisioned approach. A numerical example is then performed where the system-under-investigation is an automotive suspension component.
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Citations
Force identification by means of in-operation modal models
Eli Parloo,Peter Verboven,Patrick Guillaume,M. Van Overmeire +3 more
- 01 Jan 2002
TL;DR: In this article, a sensitivity-based method was proposed for the normalization of operational mode shape estimates on a basis of in-operation modal models only, which allows the reconstruction of complete modal model from output-only data.
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Kalman-based load identification and full-field estimation analysis on industrial test case
TL;DR: The effectiveness of the Augmented Kalman Filter algorithm 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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Advanced optimal sensor placement for Kalman-based multiple-input estimation
Roberta Cumbo,Roberta Cumbo,L. Mazzanti,L. Mazzanti,L. Mazzanti,Tommaso Tamarozzi,Tommaso Tamarozzi,Pavel Jiranek,Wim Desmet,Wim Desmet,Frank Naets,Frank Naets +11 more
TL;DR: A Kalman-based methodology is considered, which solves the problem of inverse load identification in a predictive manner and two alternative metrics are proposed, based on: i) steady-state error covariance of the estimation, ii) estimator bandwidth, with respect to the available set of measurements.
42
Practical issues on the applicability of Kalman filtering for reconstructing mechanical sources in structural dynamics
TL;DR: The present paper 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.
40
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