A Correlation-Based Distributed Fault Detection Method and Its Application to a Hot Tandem Rolling Mill Process

Abstract

In a hot tandem rolling mill (HTRM) process, the operating performance of mill stands can determine the quality of steel products, thus, should be properly monitored. Unlike previous work, which focused on a single stand or multiple stands together, this paper proposes an improved statistical fault detection (FD) method for real-time process monitoring of a target stand by considering its correlation with neighboring stands. First, it is verified that the detection performance of the traditional statistical FD method can be improved by reducing the influence of noises in process data. Then, a distributed canonical correlation analysis (CCA)-based FD method is developed to reduce the influence at the target stand by means of the compressed data transmitted from neighboring stands through a communication network. Furthermore, the proposed method can decrease the transmission and computational costs incurred by the centralized CCA method, which makes it applicable to the HTRM process. A hardware-in-the-loop simulation is finally carried out to validate the proposed method, where better monitoring performance is shown compared with the existing methods.