Dynamic Latent Variable Analysis for Multirate Closed-Loop Process Monitoring

Closed-loop control is widely adopted in industrial processes. It brings challenges for dynamic latent modeling and monitoring. Multirate process measurements in real applications even further complicate these tasks. In this article, a multirate closed-loop dynamic latent variable (M-CLDLV) scheme is proposed for DLV analysis and monitoring for closed-loop processes with multirate measurements. A new DLV model is proposed to capture the dynamic latent structure of the multirate closed-loop process data. To extract the dynamic latent structure for multirate closed-loop process data, an algorithm within the expectation–maximization (EM) framework is developed, where a weighted likelihood function is further proposed to handle the asymmetries between the fast- and slow-sampled variables. A process monitoring strategy is, thus, proposed based on the extracted multirate DLV model with comprehensive insight of dynamic and static relations. An experimental study on the deep-sea submersible shows the superiority of M-CLDLV and the introduction of the weighted likelihood function.