[PERSON] [12] investigates the fact that fuzzy preference relations (FPRs) have no additively reciprocal property and addresses their implication for decision making under uncertainty. [PERSON] [13] addresses the uncertainty measurement problem in interval-set decision tables and proposes an alternative uncertainty measure based on the conditional information entropy. [PERSON] [14] proposes a decision-making model to increase the robustness of optimal power flow in the presence of off-shore wind farms (WFs). [PERSON] [15] pointed out that any uncertain research can be divided into four parts: uncertainty representation, quantification, propagation, and management. The traditional method is a Monte Carlo simulation (MCS), based on input distributions, which samples an output distribution accordingly. Despite the fact that many complex statistical models have been proposed for uncertainty problems, MCS still remains the benchmark due to its universality and stability [16]. There has been related research on the uncertainty of state assessment. In reliability analysis (such as structural health monitoring), the uncertainty quantification domain (UQ) can provide robust decision-making information [10]. Therefore, it is important to quantify the uncertainty and choose a suitable model to study its propagation. With a prediction model based on the Gaussian regression process, [PERSON] [17] evaluated the prediction uncertainty with the data of different batches of lithium batteries. This model can not only be used to determine the credibility of the prediction model but also screen training data. [PERSON] [18] proposed a four-step analysis framework to quantitatively evaluate the impact of parametric uncertainties on an energy storage system. Combined with a global sensitivity analysis, the key parameters' influences on the system cost are analyzed and show that the proposed framework can quantitatively evaluate parameter uncertainty. [PERSON] [19] studied the uncertain factors affecting the status of wind turbines and constructed a unified assessment method with deviation and stability indicators. However, to the best of the authors' knowledge, there is currently no research on the health assessment of flight control systems under uncertain conditions. The objective of this paper is to establish a health assessment model of a flight control system under uncertain conditions and obtain the probability distribution of its health index. By comparing the results of the health assessment under certain and uncertain conditions, the validity of the proposed method is obtained. More precisely, our contribution includes: (1) A health assessment model for the flight control system under uncertain conditions is established. (2) The \(\lambda\)-PDF probability density is improved to quantify the uncertainty of the input parameters. The improved probability density function has better generalization. By changing the parameters of the probability density function, the quantization effect similar is similar to the traditional probability distribution in any finite interval. (3) MCS is used in the uncertainty propagation process to generate data with the same distribution as the original data. The rest of this paper is organized as follows. Section 2 establishes a health assessment model for a flight control system under certain conditions. Section 3 regards the model proposed in Section 2 as the uncertainty propagation process and proposes a health assessment model under uncertain conditions, including uncertainty representation, interpretation, quantification and propagation. Section 4 applies the proposed model to actual flight data to obtain assessment results. The final section analyzes the results and presents the conclusion of the whole paper.