Sparse PCA Support Exploration of Process Structures for Decentralized Fault Detection

With the ever-increasing use of sensor technologies in industrial processes and more data becoming available to engineers, the fault detection and isolation activities in the context of process monitoring have gained significant momentum in recent years. A statistical procedure frequently used in this domain is principal component analysis (PCA), which can reduce the dimensionality of large data sets without compromising the information content. While most process monitoring methods offer satisfactory detection capabilities, understanding the root cause of malfunctions and providing the physical basis for their occurrence have been challenging. The relatively new sparse PCA techniques represent a further development of the PCA in which not only the data dimension is reduced but also the data are made more interpretable, revealing clearer correlation structures among variables. Hence, taking a step forward from classical fault detection methods, in this work, a decentralized monitoring approach is proposed based on a sparse algorithm. The resulting control charts reveal the correlation structures associated with the monitored process and facilitate a structural analysis of the occurred faults. The applicability of the proposed method is demonstrated using data generated from the simulation of the benchmark vinyl acetate process. It is shown that the sparse principal components, as a foundation to a decentralized multivariate monitoring framework, can provide physical insight toward the origins of process faults.