Failure Modes and Effects Analysis (FMEA) is a very useful reliability-management instrument for detecting and mitigating risks in various fields. The linguistic assessment approach has recently been widely used in FMEA. Words mean different things to different people, so FMEA members may present Personalized Individual Semantics (PIS) in their linguistic assessment information. This article presents the design of a PIS-based FMEA approach, in which members express their opinions over failure modes and risk factors using Linguistic Distribution Assessment Matrices (LDAMs) and also provide their opinions over failure modes using incomplete Additive Preference Relations (APRs). A preference information preprocessing method with a two-stage optimization model is presented to generate complete APRs with acceptable consistency levels from incomplete APRs. Then, a deviation minimum-based optimization model is designed to personalize individual semantics by minimizing the deviation between APR and the numerical assessment matrix derived from the corresponding LDAM. This is followed by the development of a ranking process to generate the risk ordering of failure modes. A case study and a detailed comparison analysis are presented to show the effectiveness of the PIS-based linguistic FMEA approach.

失效模式和影响分析（FMEA）是一种非常有用的可靠性管理工具，可用于检测和缓解各个领域的风险。语言评估方法最近已在FMEA中广泛使用。单词对不同的人意味着不同的事物，因此FMEA成员可以在其语言评估信息中呈现个性化的个人语义（PIS）。本文介绍了基于PIS的FMEA方法的设计，其中成员使用语言分布评估矩阵（LDAM）表达对失效模式和风险因素的观点，并使用不完整的加性偏好关系（APR）对失效模式提出观点。提出了一种具有两阶段优化模型的偏好信息预处理方法，可以从不完整的APR生成具有可接受的一致性级别的完整APR。然后，设计基于偏差最小值的优化模型，以通过最小化APR和从相应LDAM得出的数值评估矩阵之间的偏差来个性化各个语义。接下来是开发排序过程以生成故障模式的风险排序。进行了案例研究和详细的比较分析，以显示基于PIS的语言FMEA方法的有效性。接下来是制定排序过程以生成故障模式的风险排序。进行了案例研究和详细的比较分析，以显示基于PIS的语言FMEA方法的有效性。接下来是开发排序过程以生成故障模式的风险排序。进行了案例研究和详细的比较分析，以显示基于PIS的语言FMEA方法的有效性。