Temporal Representation Enhanced Latent Predictive Modeling for Multivariate Process Monitoring

Fault detection in multivariate dynamic processes remains challenging because of strong variable correlations, temporal dependencies, and limited abnormal labels. Conventional statistical monitoring may be insufficient for weak dynamic deviations, while deep learning models do not always exploit compact latent representations or explicit temporal information. To address these issues, this study proposes a latent predictive monitoring framework that combines principal component analysis (PCA), learnable temporal encoding, and gated recurrent unit (GRU)-based multi-step prediction. Process measurements are projected into a compact PCA score space, augmented with explicit temporal representations, and monitored through prediction residuals between observed and predicted latent trajectories. The proposed framework is evaluated on the Benchmark Simulation Model No. 1 and further validated using industrial papermaking process data. On the BSM1 benchmark, the method achieved an average fault detection rate of 88.02 ± 2.73% with an average false alarm rate of 1.62 ± 0.15%, outperforming classical statistical methods and several deep learning baselines, particularly in slowly evolving and weakly distinguishable fault scenarios. The industrial case further demonstrated the transferability of the framework while revealing section-dependent false alarm behavior. These results indicate that integrating latent space projection with explicit temporal representation can improve the separability of prediction residuals in multivariate dynamic monitoring tasks.