【摘要】 针对传统状态评估侧重于关键生产单元,未考虑监测变量间的因果关系对状态评估精度的影响等问题,提出一种基于多变量因果驱动的复杂机电系统服役安全状态评估方法。从频域角度将广义偏定向相干分析法用于系统变量之间的因果测度分析,建立反映系统实时运行状态的因果网络模型。基于该模型,从多维统计的角度利用网络平均路径长度、聚类系数和网络结构熵提取系统的关键运行特征,并通过融合形成反映系统服役状态的综合指数。选取某化工企业实际运行过程中典型机组的故障数据进行验证,结果表明,相对于单一维度的状态评估指标,融合后形成的综合指数能够更加全面、准确地反映系统服役状态演化。更多还原

【Abstract】 Aiming at the problems that the traditional state evaluation methods focus on the key production units and do not consider the influence of the causal relationship between the monitoring variables on the evaluation results,which leads to the inaccurate results,a performance safety evaluation method for system driven by multivariate causality is proposed. The generalized partial directed coherence method is used to analyze the causal relationship of monitoring variables in the frequency domain,and a causal network model reflecting the running state of the system is established. Based on this model,the key characteristics of the system are extracted from the perspective of multi-dimensional statistics using the average path length,clustering coefficient and network structure entropy. Besides,a multi-dimensional feature fusion index reflecting the performance state of the system is established and the validity of the proposed method is verified by utilizing the fault data of a chemical enterprise. The result shows that compared with single index,the fusion feature can reflect the performance state of the system more comprehensively and accurately.更多还原