基于扩散模型的固体火箭发动机缺陷检测算法

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中图分类号：TB9;TP391 文献标志码：A 文章编号：1674-5124（2025）08-0147-08

Abstract：Asa key power device in modern military and aerospace fields，the grain of solid rocket motor （SRM） is susceptible to loadand environmental impact during storage and transportation，resulting in defects such as cracks， bubbles and debonding. These potential defects may affect the safe service of SRM. X-ray computed tomography （CT） technology，capable of providing detailed internal structural images， is an effective means of assessing the health status and safe service capability of grains. Given the challenge of the scarcity of SRM defect samples， this paper proposes an unsupervised score-based generative model algorithm tailored for SRM grain CT images for defect detection and localization. The algorithm achieves defect detection by accurately sampling complex distributions through the simulation of forward and reverse diffusion processes. Experimental results indicate that the algorithm performs well in detecting cracks， bubbles and debonding， with a defect detection accuracy of over 95% and a defect localization accuracy of over 86% . Itsadvantages in sample efficiency，along with itsrobustnessand stability incomplex scenarios，suggest that itholds significant applicable in SRM quality control and fault diagnosis.

Keywords： solid rocket motor; CT image; defect detection; score-based generative model; unsupervised learning

0 引言

固体火箭发动机（SRM）因其发射响应速度快、机动性强、易存储和维护等优点，广泛应用于战略、战术导弹及太空任务中。（剩余9958字）