In the health assessment of other aircraft systems, [PERSON] [13; 14] provided a data-driven health assessment approach for the flight control system based on fuzzy integrated evaluation and rough set reduction, with some case computations of flight data to demonstrate the method's performance. In addition, he proposed a health monitoring method of aircraft landing gear R/E System Based on the improved fuzzy c-means algorithm. The essence of this method is a data-driven cluster analysis method, and its implementation process is in the whole life process of the object, so as to highlight the significance of health monitoring. The focus of this study is the health assessment process from qualitative to quantitative. [PERSON] [15] and colleagues created an improved aircraft failure emergency management system that can be linked into the IVHM system architecture to provide real-time airborne health status assessment and automated emergency management. [PERSON] et al. [16] offered two case studies of alternative structural health monitoring technologies intended to decrease the risk of aircraft maintenance as well as the expense of frequent, lengthy inspections. In order to compensate for the absence of deterministic crack expansion analysis, [PERSON] et al. [17] designed and assessed a random crack expansion analysis approach. [PERSON] [18] and others used the Monte Carlo simulation approach to quantify the uncertainty in possible application benefits and aircraft assessment. For the issue of high failure rate and high danger of failure of hydraulic pump source systems in civil aircraft, [PERSON] et al. [19] developed a solution approach combining hierarchical analysis (AHP) and fuzzy comprehensive assessment algorithm. [PERSON] [20] added to the debate over the efficacy and feasibility of model-based systems engineering methodologies for performance verification of essential aircraft systems like flight control systems. Based on the material characteristics of the construction system, some scholars [21] have proposed a fuzzy set method to describe the uncertainty and randomness of the parameters characterizing fatigue damage and final fatigue durability. Worldwide experts and academics have largely focused on two types of health assessments for civil aircraft landing gear R/E hydraulic systems: model-based and data-based. Although the two kinds of analysis and research approaches have distinct foci, in the real-world airline application scenario, both ignore the challenge of fault detection and maintenance and instead concentrate on fault simulation or basic data analysis. The purpose of this study is to determine the health of the hydraulic system that retracts and extends the landing gear. For detecting and maintaining difficulties that must be considered in the daily operations of airlines, an improved risk coefficient is proposed based on the traditional risk coefficient [22]. The analysis process is as follows:  
1. According to the mathematical fuzzy theory, four evaluation indexes for improving the risk coefficient are obtained, which are the probability of failure (\(P\)), the severity of failure impact (\(S\)), the difficulty of detection (\(D\)), and the difficulty of maintenance (\(M\)). 2. The fault simulation data and expert evaluation data will be considered and input into the improved FCE model for data processing. 3. Propose the grade range of system health value and corresponding measures.