安全与环境学报

为解决远程数字塔台系统安全评估中主观影响和失效模式发生概率的等级区分度不足的问题，研究提出了一种改进的远程塔台系统失效模式、影响及危害度(Failure Mode, Effects, and Criticality Analysis, FMECA)分析方法。首先，通过模糊综合评判法对多名专家的评价值进行量化，降低主观因素对结果的影响；其次，将主观权重与客观权重相结合，减少失效模式的严重性评估偏差；再次，采用线性插值，增强同一发生概率等级内失效模式的区分度；最后，以远程塔台系统的26种典型失效模式为例进行分析。结果显示，全景图像拼接融合延迟和视频传输卡顿是影响系统功能的关键因素，同时验证了改进FMECA方法能够更清晰地区分失效模式的优先级。

To address the challenges of subjective bias and limited discriminative capacity in failure mode prioritization for remote digital tower system safety assessments, this study proposes an enhanced Failure Mode, Effects, and Criticality Analysis(FMECA) methodology. The framework incorporates three key improvements: fuzzy comprehensive evaluation, hybrid weighting, and linear interpolation. First, the fuzzy comprehensive evaluation method is used to quantify expert judgments through a nine-level linguistic scale, converting qualitative assessments into numerical membership matrices. This approach reduces subjectivity by aggregating evaluations from multiple experts. Second, a hybrid weighting strategy is employed, combining subjective weights derived from the Analytic Hierarchy Process(AHP) with objective weights calculated using entropy theory. This balances expert insights with data-driven rigor. Third, linear interpolation refines the discrete Occurrence Probability Rating(OPR) classification, overcoming the granularity limitations present in traditional risk prioritization methods. Experimental validation on 26 typical failure modes demonstrates the effectiveness of the proposed method. The results show that the hybrid weighting strategy reduces severity ranking deviation by 18.6% compared to single-weight approaches, thereby enhancing the objectivity of criticality assessments. The linear interpolation method enhances intra-level discrimination for mid-tier OPR categories by 22.3%, effectively distinguishing between failure modes with similar occurrence probabilities. Key high-risk failure modes, such as panoramic image fusion delays and optical sensor lags, are identified as critical threats to system stability. Additionally, the optimized Risk Priority Number(RPN) model achieves a 34.8% improvement in prioritization accuracy, allowing for clearer differentiation of failure modes with overlapping risk scores in traditional FMECA. In conclusion, the improved FMECA framework systematically addresses the ambiguity and subjectivity inherent in conventional methods. By integrating fuzzy logic, hybrid weighting, and continuous probability scoring, it offers a more robust and granular risk assessment tool for complex systems such as remote digital towers. These advancements provide practical support for optimizing system design, maintenance strategies, and safety management, ultimately enhancing operational reliability in critical aviation infrastructure.