Fault Diagnosis for Large-Scale Processes Based on Robust Multiblock Global Orthogonal Projections to Latent Structures

Abstract

Effective fault diagnosis can be obtained by using multiblock global orthogonal projections to latent structures (MBGOPLS), but there exist certain limitations in this method in terms of block division intelligence and model robustness to outliers. Although these issues can be addressed by developing correlation analysis, such as mutual information and copula-correlation, the complex coupling relationship between variables has not been fully examined. In this study, a robust MBGOPLS is proposed to intelligently diagnose faults using a relatively stable model. First, a double hierarchical clustering method is established, in which internal hierarchical clustering is performed based on Euclidean distance to discretize the samples. Subsequently, external hierarchical clustering is adopted based on mutual information distance to divide variables into different blocks, which results in intelligent block division while suppressing the influence of outliers. Second, a robust block regression coefficient matrix (BRCM) is obtained by employing joint <inline-formula> <tex-math notation="LaTeX">$\ell _{2,1}$</tex-math> </inline-formula>-norm minimization on the BRCM and prediction error of the correlation between the block input and output. Furthermore, BRCM is integrated into the MBGOPLS framework. Therefore, the proposed method is robust to outliers while retaining the diagnostic properties of MBGOPLS. Finally, the proposed method is applied in a numerical case and an actual thermal power plant. The results verify the method&#x2019;s applicability and superiority. <i>Note to Practitioners</i>&#x2014;With increasing large-scale, complex and intelligent to achieve anticipant performance, thermal power plant is prone to faults that can lead to unplanned outages. Meanwhile, complex operation mechanism and environment, and diverse data acquisition sensors make the coupling relationship between variables complex, and collected data often contain numerous outliers, which happens frequently in modern industrial process. Therefore, fault diagnosis is critical to modern industrial process, and the diagnosis accuracy and robustness are the main challenges. This forces us to ensure the block division accuracy and model robustness when using multiblock-based fault diagnosis technology. This paper proposes a robust MBGOPLS to intelligently diagnose faults of thermal power plant using a relatively stable model. Additionally, the proposed method can be extended to fault diagnosis for other large-scale processes.