In the process industry, the key performance indicator (KPI)-related prediction and fault diagnosis are important steps to guarantee the product quality and improve economic benefits. A popular monitoring method as it has been, the partial least squares (PLS) algorithm is sensitive to outliers in training datasets, and cannot efficiently distinguish faults related to KPI from those unrelated to KPI due to its oblique projection to the input space. In this paper, a novel robust data-driven approach, named advanced partial least squares (APLS), is presented to handle process outliers under an improved framework of PLS. By means of a weighting strategy, APLS can remove the impact of outliers on process measurements and establish a more accurate model than PLS for fault diagnosis in the monitoring scheme, whose effectiveness has been verified through the Tennessee Eastman (TE) benchmark process. Simulation results demonstrate that the proposed approach is suitable not only for the KPI-related process prediction but also for the diagnosis of KPI-related faults.
['Xiaochen Xie', 'Wei Sun', 'Kie Chung Cheung']
An Advanced PLS Approach for Key Performance Indicator-Related Prediction and Diagnosis in Case of Outliers