Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.

Automated summary

This study proposes an integrated fault diagnosis framework using dynamic kernel principal component analysis and cycle temporal algorithm to distinguish between sensor faults and process faults in chemical processes. By diagnosing fault variables and distinguishing the two fault types according to their characteristics, the fault detection speed and accuracy are improved. The effectiveness of the integrated fault diagnosis framework is demonstrated through application to the Tennessee Eastman process and acid gas absorption process.