Extending the dynamic response of structural input estimation by combining sensors with unmatched bandwidth

Abstract

The estimation of external loads acting on mechanic structures is of paramount importance for the design, operation, and control of these systems. In many cases, external loads cannot be measured directly due to the intrusive nature or the high costs of adequate sensors. Model-based estimation techniques yield the possibility to estimate external loads in an indirect manner using indirect measurements. These methods usually assume an optimal sensor behaviour over the entire frequency range of the estimator application, which not necessarily has to be the case due to sensor bandwidth limitations. Therefore, model-based sensor fusion is constrained to sensors and applications that share the same bandwidth. This paper proposes a novel non-intrusive approach to estimating external loading in the presence of incongruent measurement regimes of the involved sensors using Kalman filters. The method uses existing model-based estimation techniques to establish a broadly applicable framework for the fusion of multiple sensors with non-coinciding measurement bandwidths. A promising application of this new approach is not only the combination of sensors that measure the same quantity in different frequency regimes but also the combination of sensor types which are not being used together due to different measurement regimes. The newly formulated approach is tested both numerically and with a validation experiment in which accelerometer and gyroscope measurement are combined. In both cases, the new method shows improved behaviour in terms of estimations quality of an unknown force over traditional Kalman filter formulations in the presence of prominent sensor dynamics and over a wide frequency range.