一种数据驱动的故障传播分析方法

A data-driven fault propagation analysis method

大型自动化系统包含多个紧密耦合的子系统,为了增强系统的监控性能,分析故障沿子系统的传播机制是十分必要的;而现有故障传播分析方法所固有的知识爆炸问题,则是影响其在实际中有效应用的关键瓶颈。为此,本文提出一种知识导引的数据驱动故障传播分析方法。首先,证明了输入/输出指定元间的相关性包含故障传播方向信息,并通过输入/输出指定元间的相关性分析,确定故障传播关系矩阵;然后,建立输入/输出指定元回归模型,以对输出子系统的故障影响强度进行预测,从而可以分析输入子系统中发生的故障在传播过程中的危害程度;最后,应用计算机仿真来验证新方法的有效性。

It is important to analyze the fault propagation mechanism of large scale automatic system which is comprised of many tightly connected subsystems.Most existed fault propagation analysis methods are bothered by ＂knowledge explosion＂ problem,which is an obstacle for the application of these methods.A knowledge-guided data driven fault propagation analysis method is proposed in this paper. Firstly,by using correlation analysis,it is proved that correlation between the designated component（DC）of faults occurred in input and output system can tell some fault propagation information.Secondly,the fault propagation relation matrix is determined by comparing the correlation between input DC and output DC of typical faulty data. The main criterion is to set the corresponding element in the fault propagation relation matrix to be 1 when the correlation coefficient between input DC and output DC is larger.Thirdly,according to the sampling data of input DC and output DC of the case when a common fault is occurred,a DC regress model is established to predict the fault imperil level for the output system.Finally,simulation study shows its efficiency for knowledge-guided data driven fault propagation analysis method.