## 1 Introduction
[11] used the ANP approach to assign weights to traditional risk factors and to improve safety; a multi-criteria risk analysis was carried out in manufacturing systems. In 2012, [PERSON] and [PERSON] [12] applied fuzzy AHP and Fuzzy TOPSIS techniques to give weight to the conventional risk factors. [PERSON] and [PERSON] [13] used AHP to conduct a criticality analysis of a thermal power plant. RF weights were considered in fuzzy terms [14], and Multiple Objective Optimization based on Ratio Analysis (MULTIMOORA) technique was used to prioritize failure modes. In order to assess the criticality of the failuremodes of the coal-fired thermal power plant, [PERSON] et al. [15] presented the Complex Proportional Assessment of alternatives to the Gray relations (COPRAS-G) method based on uncertain data. [PERSON] et al. [16] used the hybrid MCDM approach incorporating AHP, VIKOR, and DEMATEL for the diesel engine turbocharger system. Fuzzy belief structure and TOPSIS approaches were used to normalize incomplete evaluation parameters and prioritize FMs [17]. [PERSON] et al. [18] have used the hybrid TOPSIS approach to compile subjective and objective expert judgments. [PERSON] et al. [3] used the Fuzzy belief structure-based VIKOR method to rank failure modes and risk factors given by experts. [PERSON] and [PERSON] [19] used a combination of the gray and fuzzy theory to prioritize FMs of tanker equipment. [PERSON] et al. [20] used a Qualitative Flexible (QUALIFLEX) gray rational analysis method to calculate the relative weights of the risk. [PERSON] and [PERSON] [21] applied AHP to identify the critical equipment of a thermal power plant. [PERSON]'s theory has been suggested by [PERSON] et al. [22] to prioritize the FMs of the fishing vessel system. [PERSON] et al. [23] compared Risk factor rankings by the TODIM method with the conventional RPN and IWF-TOPSIS method. In order to prioritize FMs, [PERSON] et al. [24] considered risk factors linguistically and combined interval-valued fussy set and MULTIMOORA methods were used. [PERSON] et al. [25] applied the Z-number concept to evaluate RPN and rank FMs of rotor blades of an aircraft turbine. AHP was used to determine the weights of RFs, and the rank of street cleaning vehicle FMs was given using the Fuzzy-TOPSIS (FTOPSIS) method [26]. [PERSON] et al. [27] calculated the relative weights of the RFs by the fuzzy best-worst method. Relative entropy was used to compute the weights of team members, and finally, failure modes were prioritized by the VIKOR method. [PERSON] and [PERSON] [28] applied fuzzy AHP and MULTIMOORA methods to calculate RF weights and failure modes in the steel industries. The reliability-based rough approach of VIKOR was used to prioritize the RFs of the vertical machining center transmission system [29]. [PERSON] and [PERSON] [30] used COPRAS-G and PSI methods to calculate the Maintainability Criticality Index (MCI) of each failure mode of an aluminum wire rolling mill. [PERSON] et al. [31] used AHP in the risk criterion weighting assessment, and ERVD was used to rank the FM of process plant induction motors. [PERSON] et al. [32] performed a sensitivity analysis of a multi-generation system, and in [33] performed a 4E analysis of three different configurations of a combined cycle power plant integrated with a solar power tower system to improve its performance. According to the literature review, a significant amount of effort has been made to improve traditional factor weights and MCDM methodology. FMECA is generally a group decision-making activity, with the members of the FMECA team having different skills and experiences [34]. The various FMECA team members may have ambiguous and incomplete assessment knowledge of FMs [35]. Therefore, for precise assessment, we must collect risk factor information from several team members (experts). However, in work reported earlier, failure information is collected from a single expert only.