## 2 Literature Review  
### FMEA and MCDM Methods  
A variety of studies and research have aimed to enhance the traditional FMEA by incorporating different multi-criteria and decision-making (MCDM) materials and methods [2; 3; 4; 5]. For instance, the house of reliability (HoR) approach, as outlined in [6], was introduced to manage the domino effects of failure modes. This was achieved by segmenting severity into fundamental components and conducting a cost/worth analysis using quality functional deployment (QFD). Additionally, this method categorized risk factors into two levels and seven sub-risk factors, relying on subjective expert weighting. In another study [7], the TOPSIS (technique for order preference based on similarity to ideal solution) and DEMATEL (decision-making trial and evaluation laboratory) methods were integrated. Attention was focused on analyzing the prioritization of failure modes in FMEA, considering the direct and indirect relationships between failure modes and the causes of failure, and the subjective weights of the risk factors. Furthermore, the authors introduced the intuitionistic fuzzy hybrid TOPSIS approach in [8], wherein domain experts utilized linguistic terms to subjectively weigh risk factors, while their objective ordered weights were derived through the normal-distribution-based method; this approach incorporated risk indicators like cost, time, and maintenance. The analytic hierarchy process (AHP), DEMATEL, and VIKOR (a Serbian acronym for \"Vise Kriterijumska Optimizacija I Kompromison Resenje\", meaning multi-criteria optimization and compromise solution) were combined in [9], with subjective weightage provided directly by experts. The VIKOR method pinpointed risk gaps associated with failure modes across each risk factor. Meanwhile, the AHP approach, in conjunction with DEMATEL, offered a relation map between failures and their causes, aiding in the prioritization of failure modes. The failure modes were then divided into cause and effect groups. The objective weights of the risk factors were determined using the ambiguity measure (AMWRPN) in [10], while the subjective assessments were modeled as basic probability assignments (BPAs). Virtual reality (VR) technology was adopted for identifying potential hazards in [11]. The cloud model and preference ranking organization method for enrichment evaluation (PROMETHEE) were used in [12] to rank failure causes based on considerations such as downtime duration, spare part criticality, and safety-related risks. Furthermore, the degree of uncertainty and divergence degree were used to calculate the primary and secondary weights of decision-makers, respectively. The overall weight was determined using cloud model theory. Moreover, an artificial intelligence method used the fuzzy rule base (FRB) and grey relations theory (GRT) in [13] to address the incomplete information. A fusion of risk evaluation information using the TrFNs-WAIA (weighted arithmetic interaction averaging operator of the trapezoidal fuzzy numbers) operator based on Shapley Choquet, alongside the TODIM method, was formulated to derive the risk evaluation matrix. This was carried out by simulating the psychological behavior characteristics of the team members and considering the correlations among them, and the weights of risk indicators were calculated by considering their interaction [14]. Machine learning uses an artificial neural network (ANN) model and a fuzzy inference system (FIS) to assess the failures of busbars used in the electrical vehicle industry [15]. Anintegrated-approach WLSM-MOI-partial-ranking method that incorporated the imprecision approach with a nonlinear programming model for the calculation of FRPNs was presented in [16]. The subjective weights of the risk factors were assigned by the expert judgment method. A linear programming model used the fuzzy ordered weighted geometric averaging (FOWGA) operator to assign the objective weights of the risk factors and aggregate their fuzzy ratings for calculating fuzzy RPNs in [17].