SFENOSA: A Novel KPI-Related Process Monitoring Method by Slow Feature Extraction and Elastic Net Orthonormal Subspace Analysis

Key performance indicators (KPIs), such as product quality variables or critical parameters in major units, play a crucial role in ensuring the desired performances in industrial processes. Nonetheless, focusing solely on monitoring process variables may result in the generation of nuisance alarms in response to disturbances that do not have a significant or meaningful impact on KPI variables. In this article, a novel KPI-related process monitoring method based on slow feature extraction and elastic net orthonormal subspace analysis (SFENOSA) is proposed. Traditional orthonormal subspace analysis (OSA) divides process data and KPI data subspaces into three orthonormal subspaces using least squares. To deal with the overfitting problem in high-dimensional space and enhance the robustness caused by correlated variables, the elastic net orthonormal subspace analysis (ENOSA) is developed by employing elastic net regularization in the OSA. Furthermore, to address the dynamic characteristics inherent in industrial processes, the slow feature analysis is naturally integrated into the framework of ENOSA for KPI-related process monitoring. Specifically, using the slow features extracted from process variables as the input and the KPI variables as the output, an ENOSA model is built. Based on the developed SFENOSA model, several monitoring statistics are established for KPI-related process monitoring. Experimental results on a numerical example, the well-known Tennessee Eastman process, and a real blast furnace ironmaking process demonstrate the superior performance of the proposed SFENOSA compared to the related methods.