Multivariate statistical process monitoring technologies, including principal component analysis (PCA) and partial least squares (PLS), have been successfully applied in many industrial processes. However, in practice, many PCA alarms do not lead to quality deterioration due to process control and recycle loops in process flowsheets, which hinders the reliability of PCA-based monitoring methods. Therefore, one is more interested to monitor the variations related to quality data, and detect the faults which affect quality data. Recently, a total projection to latent structures (T-PLS) model has been reported to detect output-relevant faults. In this paper, a generalized reconstruction based contribution (RBC) method with T-PLS model is proposed to diagnose the fault type for output-relevant faults. Furthermore, the geometrical property of generalized RBC is analyzed. A detailed case study on the Tennessee Eastman process is presented to demonstrate the use of the proposed method without or with prior knowledge. © 2006 IEEE.
Bibliographical noteThis work was supported in part by National 973 Project under Grant 2010CB731800 and Grant 2009CB320602 and by the NSFC under Grant 60721003, Grant 60736026, Grant 60931160440.
- Output-relevant fault diagnosis
- reconstruction-based contribution
- total projection to latent structures