A neural network symbolic approach to structural health monitoring in aerospace applications

Deep Learning models, and specifically Recurrent Neural Networks, have been successfully applied to time series classification in many applications, including Structural Health Monitoring. A relatively new field of research is the implementation of Deep Learning techniques for damage identification via measured time series in space systems, which proves chal-lenging in case of Structural Health Monitoring of large flexible structures. In this work, we propose a novel approach exploiting a symbolic time series representation as an additional data pre-processing step for data dimensionality reduction. The strategy is applied to a real world-scenario of a spacecraft hosting large solar panels equipped with distributed accelerometers at structural level, and compared against a previous benchmark case developed by the authors. Obtained results prove that the strategy has the potential to further improve classification quality towards an ideal 100% accuracy envisioned for space systems.