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

Abstract

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.

State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach

“…Given the unknown nature of this entity in the estimation context, a good assumption is that of zero th -order dynamics, i.e., zero time-derivative of , which leads to . This model has been widely adopted and validated in the literature [ 25 , 27 , 40 , 41 ], and a clearer explanation will be provided in the following. In this contribution, the estimation process is performed in discrete-time domain, and thus the discretization of the continuous-time GGL formulation expressed in Equation ( 2 ) is needed.…”

“…Starting from this layout, a coupled multibody-aerodynamic simulation was performed and position/velocity sensor data were stored by applying additive white Gaussian noise, which defines the covariance matrix R. The missing information was on the model covariance Q. In previous work [25,27], a higher source of uncertainties was considered on the load states, such that Q p Q x . This was a valuable assumption, since we can assume that the model used was accurate enough, but no information on the external loads is available.…”

“…with w p Gaussian noise associated with vector p, an important tuning parameter of the filter [25,27]. The results of the estimation can be observed in Figures 7 and 8 showing respectively the estimation of the states used as sensors and some of the unmeasured states along the blade.…”

“…The DKF operates in two estimation stages, while the AKF estimates the states and the inputs simultaneously by introducing an augmented state vector and this defines a reduction of the computational cost. The AKF has been applied in many fields and was recently demonstrated to give accurate results for automotive applications [25,27]. The results showed that, when the adopted structural model was not accurate enough, the AKF still provided a good estimation of the states at the expense of the loads' accuracy.…”

Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods

“…e technique requires a recording time, which is why the method does not perform well in real-time applications. Comparative studies between the two approaches are also seen in [18]. e response measurements are often recorded in terms of displacements, velocities, accelerations, or strains.…”