A Novel Safety Assessment Approach Based on Evolutionary Clustering Learning

机译：基于进化聚类学习的安全评估方法

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The safety risk assessment is a structured and systematic methodology aiming at enhancing the complex engineering system safety. It has been gradually and broadly used in the industrial process control system nowadays around the world. In this paper, a novel safety assessment approach based on evolutionary dictionary learning and fault tree analysis for the complex engineering system is proposed. First, historical signals are utilized to conduct the clustering learning dictionaries by norm similarity matching model and patch-based evolutionary dictionary learning algorithm. Second, the support vector machine method is employed to identify and reflect the normal and fault operating states. Third, an improved safety risks method is proposed to reflect the probable hazards of different faults on the basis of the fault tree analysis. Finally, this processing on online signals is to offer an effective safety assessment index and update the evolutionary dictionaries and safety routing metrics. The related experiments are constructed to demonstrate that our proposed approach can achieve high performance.

机译：安全风险评估是一种结构化的系统方法，旨在增强复杂的工程系统安全性。如今，它已在世界范围的工业过程控制系统中逐渐被广泛使用。本文提出了一种基于进化词典学习和故障树分析的复杂工程系统安全评估方法。首先，利用历史信号通过规范相似度匹配模型和基于补丁的进化词典学习算法来进行聚类学习词典。其次，采用支持向量机方法识别和反映正常和故障运行状态。第三，提出了一种改进的安全风险方法，在故障树分析的基础上，反映了不同故障的可能危害。最后，对在线信号的处理将提供有效的安全评估指标，并更新进化词典和安全路由指标。进行了相关实验，以证明我们提出的方法可以实现高性能。

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《Chinese Intelligent Systems Conference》|2016年|505-514|共10页
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Yi Chai; Zhaodong Liu; Hongpeng Yin; Yanxia Li;
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Safety risk assessment; Evolutionary dictionary learning; Support vector machine;

机译：安全风险评估;进化词典学习;支持向量机;

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A Novel Safety Assessment Approach Based on Evolutionary Clustering Learning

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