A Measurement-Oriented Spatio-Temporal Subspace Network for Monitoring Nonstationary Industrial Processes

A fundamental challenge in industrial instrumentation is the reliable measurement and monitoring of nonstationary processes, where the sensitivity and accuracy of traditional fault detection systems are frequently jeopardized by changing spatial and temporal variations. This paper introduces the Extended Temporal and Spatial Stationary Subspace Network (ETS3N), a novel framework designed to enhance measurement reliability by jointly modeling temporal homogeneity and spatial heterogeneity in multivariate process signals. In order to maintain the time-space-dependent coherence necessary for local stationarity assumptions, the architecture incorporates three essential units: (1) the Temporal Homogeneity Enumeration Unit (THEU), which reinforces intrinsic temporal coherence to support robust feature extraction; (2) the Spatial Heterogeneity Extraction Unit (SHEU), which constructs a triple-branch convolutional structure with functional independence and diversity constraints to decouple heterogeneous sensor responses; and (3) the Spatio-Temporal Amalgamation Decoupling Unit (STADU), which separates stationary and nonstationary components for interpretable monitoring. With the help of this deep measurement model, latent process dynamics can be accurately extracted, improving fault detection, measurement, and process state comprehension. Validation on both simulated and real-world industrial datasets, such as the blast furnace iron production process and the Secure Water Treatment (SWaT) system, shows that ETS3N outperforms current techniques in terms of sensitivity, stability, and fault isolation. The suggested method aids in the creation of future measurement systems for intricate, dynamic industrial settings.