Latent variable modeling approach for fault detection and identification of process correlations

Abstract

Techniques for monitoring process correlation structures remain to be explored, whereas significant progress has already been achieved on the monitoring of process variables. In particular, typical methods for monitoring correlation structure changes are strictly based on the process information described by the covariance matrix, and lack the ability to effectively monitor underlying structure changes. In this paper, a new approach for fault detection and identification (FDI) of process structural changes is developed, which utilizes the regression technique of latent variable modeling (LVM) to abstract principal parameters as lower-dimensional representations of the parameters in the entire dimensionality. Apart from the enhanced performance of handling the underlying connective structure information, the proposed approach can also improve fault monitoring performance owing to the more accurate confidence intervals of the regression coefficients provided in the LVM step. The effectiveness of the proposed method for the detection and identification of correlation structure changes is demonstrated for both single faults and multiple faults in the simulation studies. In addition, the relationship between the FDI of process variables and correlation structures is discussed.