Quality Monitoring and Root Cause Diagnosis for Industrial Processes Based on Lasso-SAE-CCA

Abstract

This paper is concerned with theoretical and practices approach for overall quality-related fault detection and identification in process industries. Fault detection and fault tracing can help engineers to take corrective actions and recover the process operations. A novel diagnostic method is proposed based on stacked automatic encoder-canonical correlation analysis (SAE-CCA) and least absolute shrinkage selection operator (Lasso). First, a quality monitoring scheme based on SAE-CCA is proposed, which establishes a relationship model among quality and characteristic variables to detect faults. Then, Lasso is used for locating the root causes, according to the process state and fault information. Finally, the experiments are conducted with typical industry process data, i.e., a hot strip mill process (HSMP), in order to demonstrate the effectiveness of the whole diagnosis method.