Failure mode and effect analysis (FMEA) is a multidisciplinary team-based reliability analysis tool used in a wide range of industries. However, the conventional FMEA technique has been criticized for some important deficiencies regarding risk assessments, the weights of experts and risk factors, and risk priority. Although numerous fuzzy-based modified FMEA models have been developed to improve the classical FMEA, the previous approaches lack the ability to handle internal and external uncertainties simultaneously, and they rely on extra assumptions. Hence, this study develops a novel integrated FMEA model, which can manipulate various uncertainties and unknown weights of experts and risk factors and contains more risk factors. In this model, a combination of intuitionistic fuzzy set (IFS) and rough number theory, namely intuitionistic fuzzy rough numbers, is developed to handle internal and external uncertainties of risk assessments, where IFS is used to deal with internal uncertainty, and rough number theory is employed to handle external uncertainty. Then, experts and risk factors’ overall weights involving in the FMEA technique are calculated by considering both subjective and objective aspects. In addition, a two-tier structural model containing seven risk factors is established to capture the risk information of failures more comprehensively. Finally, three practical cases to illustrate the effectiveness of the developed FMEA method are presented along with several comparisons to existing approaches. The results demonstrate that the developed method enhanced the results in terms of precise risk assessment and rational failure rankings. Additionally, its performance is superior or at least significantly compared with the original RPN, fuzzy set, intuitionistic fuzzy set, or rough number-FMEA methods.

失效模式和影响分析（FMEA）是基于多学科团队的可靠性分析工具，广泛用于各个行业。但是，传统的FMEA技术因在风险评估，专家和风险因素的权重以及风险优先级方面的一些重要缺陷而受到批评。尽管已经开发了许多基于模糊的改进FMEA模型来改进经典FMEA，但是以前的方法缺乏同时处理内部和外部不确定性的能力，并且它们依赖于额外的假设。因此，本研究开发了一种新颖的集成FMEA模型，该模型可以处理各种不确定性以及专家和风险因素的未知权重，并且包含更多的风险因素。在此模型中，直觉模糊集（IFS）和粗糙数论相结合，开发了直觉模糊粗糙数来处理风险评估的内部和外部不确定性，其中IFS用于处理内部不确定性，而粗糙数论则用于处理外部不确定性。然后，通过考虑主观和客观方面来计算涉及FMEA技术的专家和风险因素的总权重。此外，建立了包含七个风险因素的两层结构模型，以更全面地捕获故障的风险信息。最后，通过三个实际案例来说明已开发的FMEA方法的有效性，并与现有方法进行了一些比较。结果表明，所开发的方法在准确的风险评估和合理的失败评级方面提高了结果。另外，