The Failure Mode and Effects Analysis (FMEA) technique due to its proactive nature can identify failures and their causes as well as potential effects, and provide preventive/controlling measures before they occur. Nevertheless, some of the shortcomings of the FMEA technique like lack of a mental framework for considering the relationships between risks, lack of systematic perspective in confronting with risks, and weakness of Risk Priority Number (RPN) score in mathematical basis and disregarding the uncertainty of problem reduce the reliability of the outputs. In this study, an approach based on the Multi-Stage Fuzzy Cognitive Map and the Z-number theory (Z-MSFCM) is proposed to simultaneously consider the concept of uncertainty and reliability in quantities of risk factors and the weights of causal relationships in the MSFCM. Besides, a novel learning approach for Z-MSFCM has been applied based on the combination of the Particle Swarm Optimization (PSO) and S-shaped transfer function (PSO-STF) to preserve the uncertain environment of the problem. The proposed approach has been applied in a manufacturing automotive parts company and results indicate that: first, Z-MSFCM by considering the causal relationships between risks and their uncertainty and reliability in comparison with traditional RPN can provide better process-oriented insight into the impact of risks on the system; and second, the PSO-STF has high potential in generating solutions with high separability compared to Nonlinear Hebbian Learning and PSO algorithms. To put it differently, the mentioned advantages of the proposed approach can help decision-makers to analyze the problem with high reliability.

中文翻译：

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考虑不确定性和可靠性的离散生产过程中的风险评估：采用模糊学习算法的 Z 数多阶段模糊认知图

故障模式和影响分析 (FMEA) 技术由于其主动性质可以识别故障及其原因以及潜在影响，并在它们发生之前提供预防/控制措施。然而，FMEA 技术的一些缺点，如缺乏考虑风险之间关系的心理框架，在面对风险时缺乏系统的视角，以及风险优先级数 (RPN) 得分在数学基础上的弱点和无视不确定性的弱点。问题降低了输出的可靠性。在这项研究中，提出了一种基于多阶段模糊认知图和 Z 数理论 (Z-MSFCM) 的方法，以同时考虑风险因素数量的不确定性和可靠性概念以及因果关系的权重。 MSFCM。除了，基于粒子群优化 (PSO) 和 S 形传递函数 (PSO-STF) 的组合，已经应用了一种新的 Z-MSFCM 学习方法，以保留问题的不确定环境。所提出的方法已应用于制造汽车零部件公司，结果表明：首先，与传统 RPN 相比，Z-MSFCM 通过考虑风险及其不确定性和可靠性之间的因果关系，可以提供更好的面向过程的洞察力的影响。系统风险；其次，与非线性 Hebbian 学习和 PSO 算法相比，PSO-STF 在生成具有高可分离性的解决方案方面具有很高的潜力。换句话说，所提出的方法的上述优点可以帮助决策者以高可靠性分析问题。