## 1 Introduction
FTA is a quantitative and qualitative evaluation technique that identifies events as TEs and systematically arranges all the causes of errors in a top-down structure that looks like a tree to calculate the probability of the top event occurring [14]. In a conventional FTA, the BEs are represented by probabilities (crisp numbers). It assumes that the exact probability of the event has been given and adequate failure data is available. However, many modern systems are highly reliable, and it is often complicated to obtain sufficient statistics to estimate actual failure rates or failure probabilities. Moreover, the inaccuracy of system models resulting from human errors is tough to handle by simply using a conventional probabilistic reliability theory. These fundamental problems with probabilistic reliability theory have prompted researchers to find new models or new reliability theories which can complement the classical probabilistic definition of reliability. [PERSON] et al. [15] believe that when there is little quantitative information about parameter fluctuations, fuzzy methods may be the only option, and the probability of BE will be characterized by fuzzy numbers. Therefore, the fuzzy fault tree analysis was developed to deal with such issues. [PERSON] [16] used L-R type fuzzy numbers to analyze fuzzy reliability. [PERSON] et al. [17] used intuitionistic fuzzy methods to analyze fault trees on a printed circuit board assembly. [PERSON] et al. [18] proposed an FFTA approach based on the similarity aggregation method. The FFTA-HFACS method has been applied in many fields. Its differences are mainly reflected in fuzzy algorithms, the constructed fault tree, and the angle of accident analysis. [PERSON] et al. [19] applied FFTA-HFACS to hydrocarbon refueling stations for the first time, revealing that gas station accidents are associated with overfilling, drain valves, liquid outlets line, and vehicle impact. [PERSON] et al. [20] proposed FFTA-HFACS to evaluate human factors that contributed to maritime accidents. FFTA-HFACSA was used by [PERSON] et al. [21] to examine the causes of fire-explosion accidents in ships. However, to the best of our knowledge, there is no research on applying the FFTA-HFACS method in analyzing the HOFs of EMAs. The motivation of this study is to develop a flexible EMAs analysis model by incorporating FFTA into the HFACS. Based on the FT framework, fuzzy aggregation is used to form a fuzzy fault tree. Its purpose is to convert the BE qualitative data expressed by the expert's subjective evaluation of the failure possibility into a fuzzy number operation format, thereby converting the fuzzy number into a single scalar, which is used to generate the BE failure probability and further evaluate the occurrence probability of the TE. There are several aspects to the originality of this study: (i) the first application of FFTA-HFACS method in the analysis of EMAs to identify and analyze the HOFs, (ii) aggregating experts judgments for BEs of EMAs with vague failures, (iii) ranking the MCS of FFT, thereby achieving the systems reliability measures. The FFTA formed by fuzzy aggregation exhibits two advantages: (i) an ability to use expert opinions expressed in linguistic terms to evaluate EMAs' BEs; (ii) an ability to capture the subjective and imprecise factors of specialist linguistic assessments. This paper is organized as follows: Section 2 introduces the implementation framework of the FFTA-HFCAS model, and Section 3 describes the proposed methodology in detail. Section 4 demonstrates the application process of FFTA-HFACS in EMAs to confirm the applicability and flexibility. Finally, some conclusions and suggestions are presented in Section 5.