Based on the three-dimensional scanning data of solid rocket motor by industrial computed tomography

and considering the computation time and measurement accuracy

the traditional space maximum distance measuring method

named as challenge method

was improved by combining the defect features of the solid rocket motor volume space data field. The defect maximum diameter vector feature measurement method in volume space based on classified seed point method was proposed. The solid rocket motor with presetted defects was designed. Experiments show that the proposed method can improve the measurement accuracy and shorten the computation time compared with traditional challenge method

and the maximum errors of maximum diameter and axial acute angle are less than 10%

which is the foundation for solid rocket motors three-dimensional visualization fault diagnosis.