## 4 Results and Discussion
### Discussion  
1. From the quantification results of the uncertainty of each characteristic parameter in a single flight health assessment, it can be seen that the expansion method can accurately describe the uncertainty of these characteristic parameters; at the same time, the PDF curve and the histogram almost fit, which indicates that the probability distribution solution based on the maximum entropy is effective. 2. The health assessment results of 60 flights under uncertain conditions show that the aircraft is currently in good health. While only some functions slightly degrade, the health indices of flights 34 and 51 (red dots in Figure 12) are relatively low, and specifically, the health indices of these two flights' pitch and yaw channels are lower. 3. Comparing the health assessment results with and without the uncertain factors in Figure 12, the health index has basically the same trend, but the health index under certain conditions fluctuates more. However, for a product with a long life cycle, such a large fluctuation in health status in a short period is obviously not in line with the law of life degradation. Therefore, the assessment results under uncertain conditions are more in line with the actual situation. 4. In addition, a larger health index fluctuation means higher false alarm rates. Taking flights 34 and 51 as an example, the health indices are 0.4524 and 0.4883 without considering the uncertainty, which has reached the severe decline threshold; however, under uncertain conditions, the health indices are 0.6838 and 0.6562. Although these results are relatively low compared with other flights, the aircraft is still in a slightly degraded state and has not reached a state ofsevere decline. Therefore, health assessments under uncertain conditions reduce the influence of outliers on the assessment results. 5. As mentioned at the beginning of Section 2, the research in this article is based on the authors' previous research [14]. In this previous piece of research, we established a health assessment model of a flight control system under certain conditions, obtained the results of the flight control system under certain conditions and compared them with the results obtained by using AHP, as shown in Figure 13. The conclusion is that the built model can reduce inaccurate evaluation caused by the bias of subjective judgments of humans, which improves the reliability and robustness of evaluation results. Based on the previously established certain comprehensive evaluation model, this paper establishes an evaluation model for a flight control system under uncertain conditions and compares the evaluation results obtained under certain and uncertain conditions. The conclusion is that the health assessment model of the flight control system under uncertain conditions can reduce the influence of outliers on the assessment results. Figure 13: Comparison of AHP and comprehensive assessment. Therefore, by establishing health assessment models under certain and uncertain conditions successively, the influence of human factors and outliers on the assessment results is reduced and the false alarms caused by these factors are therefore reduced. In this paper, a health assessment model for flight control systems under uncertain conditions is established. The research ideas of this model are also applicable to other large-scale systems. That is, an assessment model under certain conditions is first established, whose process includes establishing health assessment indicators, dividing health levels, selecting assessment models and choosing a weight distribution method. Then, uncertainty is introduced into the evaluation process, and uncertainty problem research, including uncertainty expression, uncertainty quantification, uncertainty propagation, etc. is used. Finally, the system's health assessment results under uncertain conditions are obtained.