Open Access

ARTICLE

Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

by Gang Xiang^1,2, Xiaoyu Cheng³, Wei He^3,4,*, Peng Han³

1 Beihang University School of Automation Science and Electrical Engineering, Beijing, 100083, China
2 Beijing Aerospace Automatic Control Institute, Beijing, 100070, China
3 School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China
4 Rocket Force University of Engineering, Xi’an, 710025, China

* Corresponding Author: Wei He. Email:

Computer Systems Science and Engineering 2023, 47(1), 273-298. https://doi.org/10.32604/csse.2023.037892

Received 20 November 2022; Accepted 03 March 2023; Issue published 26 May 2023

Abstract

A liquid launch vehicle is an important carrier in aviation, and its regular operation is essential to maintain space security. In the safety assessment of fluid launch vehicle body structure, it is necessary to ensure that the assessment model can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process. Therefore, a belief rule base with interpretability (BRB-i) assessment method of liquid launch vehicle structure safety status combines data and knowledge. Moreover, an innovative whale optimization algorithm with interpretable constraints is proposed. The experiments are carried out based on the liquid launch vehicle safety experiment platform, and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform. The MSEs of the proposed model are 3.8000e-03, 1.3000e-03, 2.1000e-03, and 1.8936e-04 for 25%, 45%, 65%, and 84% of the training samples, respectively. It can be seen that the proposed model also shows a better ability to handle small sample data. Meanwhile, the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings, which indicates the interpretability of the BRB-i model. Experimental results show that, compared with other methods, the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

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Keywords

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