A Novel Dynamic Principal Component Analysis Based on Block Dynamic Mode Decomposition for Chemical Process Fault...

Dynamic principal component analysis (DPCA) has been widely adopted for process monitoring and fault detection in dynamic systems. However, its application in chemical process industries remains constrained by several limitations, including vulnerability to intermittent disturbances caused by equipment vibrations, inability to effectively capture long-term time dependence, and lack of consideration for subsystem coupling. To overcome these limitations, we propose an enhanced methodological framework. First, a frequency-based clustering technique is introduced to remove intermittent noise caused by equipment vibrations. Second, a novel matrix augmentation strategy is introduced to improve DPCA's capacity to capture long-term time dependence. Third, block matrix dynamic mode decomposition is introduced to model the subsystem coupling. Experimental results demonstrate that the proposed method achieves competitive performance under moderate lag settings and superior fault detection performance under large lag settings, with advantages in detecting correlated faults across multiple subsystems. In addition, ablation experiments demonstrate that each improvement plays a positive role, and their integration leads to performance gains in complex industrial environments.