論文ID: 21-00380
Since the early days of spacecraft development, accurate and simple vibro-acoustic prediction of equipment on the spacecraft panels subjected to acoustic excitation has been conducted in order to mitigate the over-conservative environmental test conditions. The conventional prediction methods are based on numerical solution of equation of motion, such as FEM/BEM and SEA, the so-called deductive approach. However, in a spacecraft with complex structures, there are many structural and non-structural objects, such as wiring harnesses, connecting cables and electronic boards in the equipment, which are usually difficult to be modelled into these methods. These un-modeled objects are usually treated by uncertainty of models, which always results in overly conservative prediction. In order to mitigate this uncertainty caused by model limitation of deductive approach, this study proposes a more accurate and simple inductive approach for vibro-acoustic prediction using Gradient Boosting Decision Trees (GBDT), which is one of the machine learning algorithms based on measured data. In addition, in order to take into account the vibration modes of the structural panels and waveform trends of vibration response spectrum in the creation of the learning model, explanatory variables based on the design drawing information were added, and the concept of bidirectional recurrent neural networks (BRNN), which is used for predicting time histories waveforms, was incorporated. This approach was applied to the vibro-acoustic prediction using the measured data of the equipment on the spacecraft panels in the acoustic tests of 7 spacecrafts developed by JAXA, and the results showed that this approach can make a reasonable prediction with the uncertainty margin mitigated by about 2 to 4 dB compared with the conventional approach.
