Fine-Grained Monitoring With Orthogonal Projection to Dynamic Latent Structure Model for Closed-Loop Industrial IoT

In closed-loop Industrial Internet of Things (IIoT), not all deviations lead to the process out of control due to the compensation of controllers. Normal operating condition changes, dynamic anomalies with unchanged operating conditions, and complete loss of control can be finely distinguished by monitoring static or dynamic components of the process. However, existing methods often suffer from overlapping decomposition of dynamic and static components with limited interpretability, which causes information leak and reduces monitoring performance. To address these issues, this paper presents an orthogonal projection to dynamic latent structure (OP-DLS) method, enabling fine-grained process monitoring that distinguishes different types of deviations. Specifically, by jointly minimizing the dynamic unpredictable variance and the static reconstruction error, OP-DLS maps process data onto non-overlapped dynamic and static subspaces through an orthogonal projection to prevent information leak. In addition, the temporal dependence and representative properties of dynamic components are explicitly modeled for better generalization and interpretability. Building upon this, targeted monitoring indices are designed to monitor the dynamic, static, and global aspects of the process for fine-grained distinction of different types of deviations. Compared with existing methods, the superiority of our proposed method is validated through a numerical example and an industrial-scale three-phase flow facility process.