$\ell _{2,1}$ -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’s applicability and superiority. Note to Practitioners—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.">

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

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