Total Structure Multirate Autoregressive Dynamic Latent Variable Model for Multirate Dynamic Process Fault Detection
提出一种全结构多速率自回归动态潜变量模型，通过全局和局部潜变量捕捉多速率数据的自相关与互相关，用于故障检测，在多相流和造纸废水数据上验证了有效性。
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.