## 2 Background Research  
In this section, a comprehensive literature review is presented, focusing on several studies that have proposed hybrid models to address the problem of relative weighting between FMEA risk variables and their different levels of complexity. This topic is particularly relevant as the industry tends to favor simpler, more user-friendly models and often disregards more complex solutions despite their greater potential for robust problem-solving capabilities. Failure mode and effects analysis (FMEA) is widely used in industry to identify, evaluate, and prioritize failure modes in systems and processes with the aim of reducing risk and improving reliability. Traditional FMEA methods, while simple and effective in many cases, struggle with uncertainty, subjectivity, and complex system dependencies. Hybrid models that integrate techniques such as fuzzy logic, artificial intelligence (AI), and Bayesian networks have evolved to overcome these limitations. Despite their potential, the implementation of hybrid FMEA models poses significant challenges to the industry, ranging from technical difficulties to organizational barriers. Traditional FMEA uses three key factors--severity (S), occurrence (O), and detection (D)--to calculate the risk priority number (RPN), which helps prioritize failure modes based on risk. While effective, this approach suffers from subjectivity in the assignment of S, O, and D values and a lack of sensitivity to uncertainties inherent in complex systems [17]. The traditional RPN calculation does not fully capture the dependencies between failure modes and does not provide a method to account for uncertainties in expert judgment. Furthermore, the linear multiplication of S, O, and D can lead to misleading risk priorities, highlighting the need for more sophisticated approaches [18]. Hybrid FMEA models integrate various methods to overcome the limitations of traditional FMEA. These include fuzzy logic, which addresses the subjectivity and uncertainty of FMEA assessments, and machine learning techniques, which use historical data to predict failure modes more accurately. Fuzzy logic allows FMEA to incorporate linguistic terms such as \"low\", \"medium\", and \"high\" and convert them into fuzzy numbers to better handle the inherent ambiguity in risk assessments [19]. This approach refines the RPN calculation and provides a more nuanced understanding of risk in uncertain environments. Bayesian networks are probabilistic models that dynamically update risk predictions based on new data, allowing the industry to continuously refine its failure mode predictions. This method is especially useful for complex systems where failure modes are interdependent [20]. Neural networks and AI-based models improve FMEA by learning from historical failure data and improving prediction accuracy. [PERSON] et al., for example, developed an FMEA model based on neural networks that adapts to changes in system behavior and thus represents a dynamic tool for risk management [21]. The hybridization of FMEA increases its complexity but significantly improves its usefulness for modern, data-driven industrial applications. However, this complexity also brings with it numerous implementation challenges [22]. Implementing hybrid FMEA models requires sophisticated technical expertise, particularly in dealing with advanced methods such as fuzzy logic, Bayesian networks, and AI. These techniques require specialized knowledge that is not available in many industries [23]. For example, fuzzy FMEA involves the construction of membership functions and fuzzy inference systems, which require a deep understanding of fuzzy set theory [20]. Hybrid FMEA models rely heavily on data, particularly historical failure data and system performance metrics. In industries where such data are unavailable or incomplete, it becomes difficult to train machine learning models or construct Bayesian networks [20].