Total Structure Multirate Autoregressive Dynamic Latent Variable Model for Multirate Dynamic Process Fault Detection

Traditional process monitoring methods often rely on data with uniform sampling rates, which may lead to the loss of valuable information across both time and space dimensions. Moreover, multirate data exhibits strong autocorrelation and cross-correlation among various sampling rates. Effectively capturing these characteristics is crucial for accurately monitoring process variations. In this article, a total structure multirate autoregressive dynamic latent variable (Ts-MARDLV) model is proposed, which establishes global dynamic latent variables for all measurements and local static latent variables for each sampling rate, effectively analyzing the autocorrelation and cross-correlation of samples. For multirate process monitoring, the Ts-MARDLV model-based fault detection schemes are developed. Three diverse fault detection statistical metrics are constructed to monitor faults in different latent spaces. The proposed method is validated on multiphase flow datasets and a real papermaking wastewater process, demonstrating its superior effectiveness compared to single or multirate methods.