关键词: 过程系统, 主元分析, 偏最小二乘法, 残差, 故障早期识别

Abstract:

Traditional data-driven process monitoring methods mainly ignore the consideration of process mechanism. The predictive residuals-based process monitoring methods realize the approximation of the local process mechanism by a data-driven regression model, and use the residual of the regression model as a feature to better monitor the abnormal deviation of the process. But their approximation of the local process mechanism ignores the specific process information. As a form of process information, process topology can be used to extract the process and causal relationship between variables. Based on this, a statistical process monitoring method based on process topology information is proposed in this paper. Considering specific process information, a modified index for variable selection is proposed by extracting the process and causal relationship between variables from the flowchart. On this basis, a process monitoring model based on prediction residuals is established to monitor the process deviation. The proposed method combines specific process information with a data-driven regression model to achieve a more accurate approximation of the local process mechanism. The proposed method is applied to a continuous reforming unit, the result shows that the method proposed in this paper can detect fault earlier compared to process monitoring method based on prediction residual or PCA (principal component analysis) model.

Key words: process systems, principal component analysis (PCA), partial least square (PLS), residual, early fault identification