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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The interaction between the rotating blades and the external fluid in non-axial flow conditions is the main source of vibratory loads on the main rotor of helicopters. The knowledge or prediction of the produced aerodynamic loads and of the dynamic behavior of the components could represent an advantage in preventing failures of the entire rotorcraft. Some techniques have been explored in the literature, but in this field of application, high accuracy can be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This paper applies the Kalman filtering technique to rotor load estimation. The nature of the filter allows the usage of a minimum set of sensors. The compensation of a low-fidelity model is also possible by accounting for sensors and model uncertainties. The efficiency of the filter for state and load estimation on a rotating blade is tested in this contribution, considering two different sources of uncertainties on a coupled multibody-aerodynamic model. Numerical results show an accurate state reconstruction with respect to the selected sensor layout. The aerodynamic loads are accurately evaluated in post-processing.

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The knowledge of the dynamic behavior of a mechanical system in a certain operating scenario is essential in many industrial applications. In particular, nowadays, the accurate and concurrent identification of the response fields and external loads represents a challenging target. Several experimental techniques, some exploiting a coupling with simulated solutions based on predictive methodologies, have recently been proposed and are in current use. However, in practice there is a common issue in the selection of the optimal types of sensors and their measurement location selection in order to reconstruct the desired quantities (e.g., loads, displacement or acceleration field) for a desired accuracy and dynamic range. This paper focuses on a Kalman filter approach for multiple input/state estimation, combining operational measurement and numerical model data. In the presented framework, an existing Optimal Sensor Placement (OSP) strategy for load identification is discussed and an improvement of this sensor selection is proposed. The reference OSP approach, previously proposed by the authors, is mainly focused on system observability, which is only a minimum requirement to obtain a stable estimator. For this reason it does not necessarily lead to the most accurate estimator or the highest dynamic range. In this work, we propose two alternative metrics based respectively on estimator covariance convergence and closed-loop estimator bandwidth with respect to the available set of measurements. The existing OSP is compared with the proposed metrics for multiple input/state estimation, showing improved accuracy of estimated quantities when these new metrics are accounted for in the sensor selection.

Rotorcraft blades are subject to significant dynamic loads both in standard and critical operating conditions. The knowledge and the prediction of the produced aerodynamic loads could represent an advantage in preventing failures on the rotorcraft, but also to avoid unnecessary inspections and reduce the downtime of the aircraft. This work applies the K´alm´an filtering technique to estimate the aerodynamic loads on a helicopter rotor blade at wind-tunnel model scale, representative of that of a medium-weight helicopter (900 mm span and 72.5 mm chord, corresponding to a 1:5-1:8 model scale). The loads estimation is based on strain measurements provided by Fibre Bragg Grating sensors embedded in the blade at several spanwise sections. Two different test campaigns have been done: a static one to characterize the experimental set-up followed by a wind-tunnel test campaign. The results show that the Fiber Bragg Grating sensors could represent an alternative choice with respect to strain gauges for strain measurements in in-flight health monitoring.