A Multiblock Kernel Dynamic Latent Variable Model for Large-Scale Industrial Process Monitoring

Abstract

For industrial processes, the multivariate statistical process monitoring (MSPM) technique is an effective way to perform data-driven process modeling and fault detection. Compared to static multivariate statistical models (SMSMs), dynamic multivariate statistical models (DMSMs) can effectively model the autocorrelation and intercorrelation of data and improve the performance of fault detection. However, the performance of using DMSMs degrades when one uses data collected from large-scale processes, as it cannot effectively model the local behavior of large-scale processes. In contrast, multi-block methods can reflect these local behaviors well. Meanwhile, considering that kernel mapping is a proven technique for revealing nonlinear relationships in large-scale data. In this paper, a multivariate statistical model based on the multi-block kernel dynamic latent variable (MBKDLV) is proposed to monitor large-scale industrial processes. It divides high-dimensional process variables into blocks and monitors the local behavior by analyzing these blocks’ dynamic and static relationships. MBKDLV-based process data modeling is composed of in-block and inter-block algorithms, which also consider in-block and inter-block nonlinearity and correlations. Three statistics are proposed for monitoring the local behavior of large-scale processes. The effectiveness of the proposed approach is illustrated by a case study of a hot strip mill process (HSMP).