Neighborhood based global coordination for multimode process monitoring

• A novel clustering algorithm is proposed based on serial correlations.
• Local models are aligned together into a global one for process monitoring.
• Both local and nonlocal information are utilized during dimension reduction.
• Both within-mode and cross-mode correlations are involved in the global monitoring model.

A novel framework named neighborhood based global coordination (NBGC) for model alignment and multimode process monitoring is proposed in this paper. To identify the different patterns in the training database, a new clustering method is derived by utilizing the serial correlations between adjacent samples. With local outlier probability (LoOP) which can exhibit the novelty of the augmented samples, the fracture parts between multiple modes can be located. Then, an arrangement approach is conducted to piece together the similar but disconnecting segments of samples. Next, conventional principal component analysis (PCA) is applied for each separated cluster. Different from the traditional approaches where process monitoring will be performed individually and results from all local models will be summarized, the proposed method aims at involving the inter-mode correlations by aligning the local models together into a global model. A new objective function is proposed to ensure that both the local and nonlocal information can be included. Finally the utility and feasibility of NBGC are demonstrated through a numerical example and TE benchmark process.