A data-driven fault propagation analysis method

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. © All Rights Reserved.