A health status estimation method based on interpretable neural network observer for HVs

Estimating the health status is a crucial step in learning about the health of hypersonic vehicles beforehand. The estimation results can be used to detect abnormal states and provide data reference for fault diagnosis. However, certain conventional neural network-based estimate techniques rely heavily on data and have limited model interpretability, which challenges the accuracy of the estimation results. This research aims to address the problems of data dependency and model interpretability in estimation models. In this study, a block interpretable neural network model with constraints on the trajectory and attitude equations is established. On the basis of the interpretable neural network model, two health status estimation methods are proposed: one that is unsupervised and the other that is supervised. Additionally, in the supervised health status estimate approach, an FC-LN-Mish structure is created to fit the relationship between the fault residual and the fault state parameters. The results indicate that the proposed estimation methods can fit the system mechanism relationship more accurately, improve the model interpretability, reduce data dependency, and ensure high estimation efficiency and precision. The FC-LN-Mish structure can reduce the missed detection rate and false detection rate to some extent, and perform better than other models under the low fault deviation degree. In conclusion, the interpretable neural network model-based observers accurately observe the health status parameters of rudders and RCS, reduce data dependence and data processing costs, and have better performance under high uncertainty interference. It provides effective method for online health estimation. ●An interpretable neural network model is established for hypersonic vehicles.●Unsupervised parameter observer is designed based on interpretable model.●It provides an effective method for fault detection under incomplete data sets.●It reduces the missed detection rate and false detection rate under the less data.