Root cause analysis of faults in cement pre-decomposition system using kernel principal component analysis and multi-scale symbolic transfer entropy

The industrial production process is complex, and when a fault occurs, multiple variables deviate from their normal state. In this paper, a solution is proposed which utilizes correlation and causality analysis to identify the root cause of failure at an early stage. Kernel principal component analysis (KPCA) demonstrates excellent monitoring performance in nonlinear systems. By applying the unified contribution graph method to KPCA, the main fault variables can be identified. Additionally, the Multi-Scale Symbolic Transfer Entropy (MSTE) method is employed to construct a delay causality diagram, addressing non-stationarity and lack of time-lag analysis. It considers time delays between variables while excluding indirect causality to diagnose the root failure point. The effectiveness of KPCA-MSTE is further validated by analyzing the failure process of a cement predecomposition system in actual production.