###### Abstract
Generally, practical applications involve the conceptualization of the problem by integrating linguistic uncertainty and the use of data by integrating stochastic uncertainty. Therefore, it is needed to design a unified platform to process uncertainties that contain vague and stochastic characteristics using statistical analyses and linguistic interpretation [21]. The use of Bayesian networks (BNs) has been integrated into improved FMEA versions integrating stochastic uncertainty and conditional probabilities into models. A BN has an advantage in dealing with uncertainty [22]. Therefore, a good FMEA has the necessary features to build a good BN: a bottom-up (or top-down) analysis of all the components, subcomponents and cause-failure-effect chains [23]. Moreover, the work of [24] presented the use of a BN as a new methodology for encoding the design of FMEA models of mechatronic systems. In the same way, the authors of [25] combined FMEA and Fault Tree Analysis based on a BN to form a fault diagnosis analysis model to conduct quantitative failure diagnoses based on the given priori probability and the specific posterior evidence. Additionally, the authors of [26] developed a dynamic and comprehensive quantitative risk analysis approach for the accident scenario and risk modeling of natural gas stations based on a bow-tie diagram and a BN to model the worst-case accident scenario and to assess the risks. Likewise, the work of [27] implemented a prototype of a hybrid expert system for the diagnosis of embedded software by integrating a case-based reasoning with a BN through FMEA-style case-based reasoning (F-CBR) with the corresponding failure spectra as the bridge. They used GeNlE 2.0 software to achieve dynamic reasoning results. Finally, BNs are used to identify the most critical failure causes in the transistor and metal-oxide semiconductor field-effect capacitor. As a result of BN analyses, high temperature and overvoltage are distinguished as the most crucial failure causes [28]. In these works, multiple failure modes and effects are related in a BN, and the conditional probability of the network represents the output variable. In these cases, the linguistic uncertainty is not considered, unlike our research where linguistic uncertainty and stochastic uncertainty are used, in addition to the Bayesian theorem being used as a mechanism of attention to determine the posterior probabilistic of the input variables without making use of a BN as in the investigations cited. Regarding the integration of FMEA with FIS and BN, there needs to be more document publications in the literature. The authors of [29] compared the results of the FMEA and the subjective probability values of its improved proposal integrating the diffuse Bayesian network (FBN) and the best-worst diffuse method (FBWM) to evaluate failures in plastic production. In this research, they use fuzzy membership functions to analyze input variables to the system. They then use a method to weigh the best and worst factors, where the fuzzy domain is used as a probabilistic domain without theoretical support, unlike our proposed approach, where Gaussian Probabilistic Function is used to represent input variables and to give consistency to the use of the conditional probability theorem of Bayes as a mechanism of attention and relation or to weight the input variables to the system. Additionally, the work of [30] proposed a novel FMEA method by integrating the Bayesian Fuzzy Assessment Number (BFAN) and extended gray relational analysis-technique for order preference by similarity to the ideal solution (GRA-TOPSIS) method. In this research, they also use fuzzy membership functions to analyze input variables to the system. They then design a probability density function for each membership function and, finally, across the product between the estimated probability and the diffuse value, find the probabilistic fuzzy membership function.