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Knowledge modeling for root cause analysis of complex systems based on dynamic fuzzy cognitive maps

机译：基于动态模糊认知地图的复杂系统根本原因知识模型

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This paper proposes a knowledge model for root cause analysis (RCA) of complex systems based on fuzzy cognitive maps (FCMs) and particle swarm optimization algorithm (PSO). The process knowledge and experience of technicians can be captured by FCMs that are characterized by briefness of knowledge modeling and execution. The traditional methods for RCA based on FCMs are restricted to fixed incidence matrix. However, the individualized features are there existing in each system of the same kind, therefore fixed weights are unreasonable. PSO is introduced to detect the weight that can reveal the individualized features of systems among concepts of FCMs. And then a dynamic knowledge model for RCA is obtained, including predictive, diagnostic, and hybrid RCA. The three types RCA can be used for forecasting future event of output, identifying root cause and presenting measures of abnormal event. The effectiveness of proposed method is validated in aluminum reduction process, and the experiments results show the proposed method is effective and application potential.
机译：本文提出了基于模糊认知地图（FCMS）和粒子群优化算法（PSO）的复杂系统的根本原因分析（RCA）的知识模型。技术人员的进程知识和经验可以被FCMS捕获，其特征在于知识建模和执行的简要性。基于FCMS的RCA传统方法仅限于固定发射矩阵。然而，个性化特征在同一种类的每个系统中存在，因此固定权重是不合理的。引入PSO以检测可以揭示FCMS概念之间系统的个性化特征的权重。然后获得RCA的动态知识模型，包括预测性，诊断和杂种RCA。三种类型的RCA可用于预测输出的未来事件，识别根本原因和异常事件的呈现措施。所提出的方法的有效性在铝还原过程中验证，实验结果显示了所提出的方法是有效的和应用潜力。

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