<p>In the aerospace sector, real-time, accurate, and interpretable health status assessments enable decision makers to make timely and accurate decisions. Belief rule base (BRB) is widely used in aerospace equipment health assessment due to its excellent qualitative and quantitative information processing capabilities. However, differences in the expert knowledge reliability can lead to differences in the extent to which parameters need to be optimized and over-optimization of parameters reduces interpretability. Therefore, an aerospace equipment health assessment method based on interpretable belief rule base with fuzzy credibility factor (IBRB-c) is proposed. First, a fuzzy credibility factor characterizing the expert knowledge accuracy is proposed to address the problem of not being able to quantify the extent to which parameters need to be optimized. Second, four new strategies for implementing model-interpretable optimization are proposed to address the problem of over-optimization of parameters. Finally, the IBRB-c is validated via lithium-ion battery and liquid launch vehicle experiments.</p>