Abstract
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.
Highlights
The main goal of the chemical industry is to improve the efficiency and accuracy of manufacturing facilities
The distinction between sensor and process faults has always been an important part of chemical process fault diagnosis
This paper proposes an integrated fault diagnosis model that can effectively distinguish between sensor faults and process faults in modern chemical processes
Summary
The main goal of the chemical industry is to improve the efficiency and accuracy of manufacturing facilities. The principal component analysis (PCA) in data-based methods is widely used for process and sensor fault diagnosis in chemical processes [14,15]. A cycle temporal algorithm (CTA) was proposed based on traditional temporal logic to solve a large amount of chemical data and the problem of low calculation accuracy. By combining it with the DKPCA method, it can capture most of the fault information in the system and improve the detection accuracy. The cycle segmentation of the model data is completed to reduce the dimensionality of the matrix, improving the calculation accuracy and eliminating the calculation redundancy
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