Multiple Model Filtering for Failure Identification in Large Space Structures

Abstract

Future space structures, while obeying the low-mass-to-orbit requirement, are expected to drastically increase their size. As redundancy in structural elements is typically not allowed, the risk of failures increases, and information about damaged parts could be useful to avoid excess loads. Furthermore, as distributed active control is likely to be included, new schemes could be envisaged to bear with detected failure(s). Identification of weakened elements in these large structures is indeed of paramount importance. Bayesian estimators, intrinsically capable to work with noisy data provided by sensors, can be a suitable tool for monitoring the health of structure. The paper investigates the performance of these estimators for failure detection and identification problems referring to a rich, realistic model of a future large radar satellite. Multiple models built on different possible behaviors are considered together to timely set the alarm when the likelihood threshold for a possible failure is passed. The capability to identify differently located occurrences is analyzed, discussing the confidence in the solution. Aside from the well-known literature on Bayesian estimators, focus is on the hints which could be gained from realistic simulations in view of the possible operational applications to space structures.