Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach

Based on an argument that some process variables can influence other process variables with time-delays, dynamic decentralized principal component analysis (DDPCA) was recently proposed for modeling and monitoring dynamic processes, and it has achieved superior monitoring performance than its counterparts, such as dynamic PCA and dynamic latent variables (DLV). Although experimental results have demonstrated the promise of selecting dynamic feature (ie., auto-correlated and cross-correlated variables with time-delays) for each measured variable in handling dynamic process data, it can be easily verified that the dynamic feature selection suffers from a proper determination of a cutoff parameter. To tackle this issue, an alternative formulation of DDPCA through using variable-weighted method is proposed. The dynamic feature is characterized individually by assigning different weights to different variables with time-delays. The weighted variables are then used to form a block corresponding to each variable, fault detection and diagnosis are thus implemented based on these block PCA models. The superiority of the proposed weighted DDPCA (WDDPCA) method over dynamic PCA, DLV, and DDPCA are explored by two industrial processes. The comparisons apparently illustrate the salient monitoring performance that can be achieved by WDDPCA.