Causal network construction based on KICA-ECCM for root cause diagnosis of industrial processes

Abstract

Root cause diagnosis is able to find the propagation path of faults timely when the fault occurs. Therefore, it is of key significance in the maintenance and fault diagnosis of industrial systems. A commonly used method for root cause diagnosis is causal analysis method. In this work, a causal analysis method Extended Convergent Cross Mapping (ECCM) algorithm is used for root cause diagnosis of industry, however, it has difficulties in dealing with large amounts of steady state data and obtaining accurate propagation paths. Therefore, a causal analysis method based on Kernel Independent Component Analysis (KICA) and ECCM is proposed in this study to deal with the above problems. First, the KICA algorithm is used to detect faults to get the transition process data. Second, the ECCM algorithm is used to construct causal relationship among variables based on the transition process data to construct the fault propagation path diagram. Finally, the effectiveness of the proposed KICA-ECCM algorithm is tested by using the Tennessee Eastman Process and Industrial Process Control Test Facility platform. Compared with the ECCM and GC algorithm, the KICA-ECCM algorithm performs better in terms of accuracy and efficiency.