Spatiotemporal Local Analysis for Nonlinear Dynamic Process Monitoring

Modern industrial processes are becoming increasingly complex, exhibiting pronounced nonlinear dynamic characteristics that pose significant challenges for traditional monitoring approaches. Moreover, existing methods for nonlinear dynamic problems often fail to preserve the spatiotemporal characteristics of process data, resulting in suboptimal feature extraction that inadequately represents the original data’s temporal dynamics and spatial correlations, ultimately degrading monitoring performance. To address these limitations, this study proposes a spatiotemporal local analysis (STLA) framework. This method builds neighborhood relationships from temporal and spatial perspectives and uses an autoencoder (AE) to approximate the local features of original data, thus enabling the features to better retain the original information and improve monitoring performance. Finally, a numerical example, the benchmark Tennessee Eastman (TE) process, and an actual power plant process demonstrate the superiority of the proposed method. The proposed method achieves enhanced performance with an improved fault detection rate (FDR) and reduced false alarm rate (FAR).