In-operation modal analysis has become a valid alternative for structures where a classic forced-vibration test would be difficult if not impossible to conduct. The modelling of output-only data obtained from naturally excited structures is particularly interesting because the test structure remains in its normal in-operation condition during the test. One of the drawbacks of in-operation analysis is that part of the modal parameters can no longer be estimated. Consequently, the applicability of in-operation modal models remains somewhat restricted. For some in-operation applications, interest lies in the identification of forces that gave rise to measured response signals. In order to solve this ill-conditioned problem, a complete modal model of the structure is required. Recently, a sensitivity-based method was proposed for the normalization of operational mode shape estimates on a basis of in-operation modal models only. This method allows the reconstruction of complete modal models from output-only data. In this contribution, the possibility of using such re-completed in-operation modal models for the identification of localized forces is explored.

Force identification by means of in-operation modal models

Multibody model based estimation of multiple loads and strain field on a vehicle suspension system

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

Advanced optimal sensor placement for Kalman-based multiple-input estimation

https://hal.archives-ouvertes.fr/hal-02068530/document

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

https://lirias.kuleuven.be/bitstream/123456789/546342/2/Tamarozzi+et+al.+-+2016+-+Noise%2C+ill-conditioning+and+sensor+placement+analy.pdf

Noise, ill-conditioning and sensor placement analysis for force estimation through virtual sensing

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.

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

Virtual sensing of wheel center forces by means of a linear state estimator

Improving vehicle passenger safety is of major importance in modern automotive industry. Within this framework, vehicle stability controllers play a key role, as they actively contribute to maintain vehicle driveability even in potentially dangerous situations. An example of such a controller is Electronic Stability Control (ESC), that brakes individual wheels to generate a direct yaw moment to stabilize the vehicle (e.g. from excessive understeer or oversteer). This paper presents the real-time implementation of a stability controller based on measured (and/or estimated) yaw rate and sideslip angle and on phase-plane related stability criteria. The control strategy is first developed in MATLAB-Simulink environment with a simplified vehicle model. Then, the controller is assessed via software-in-the-loop using a full vehicle model developed in Simcenter Amesim, before implementing it on a real-time platform. Results are promising, endorsing the implementation of hardware-in-the-loop using an Electronic Control Unit.

Real-time implementation of yaw rate and sideslip control through individual wheel torques

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