A Covariate-Regulated Sparse Subspace Learning Model and Its Application to Process Monitoring and Fault Isolation

Abstract

Multivariate functional data are increasingly common in various applications. The cross-correlation of different process variables is typically complex in that a variable might be weakly correlated or not correlated with most of the other variables, and the cross-correlation is time-varying and might be regulated by some exogenous covariates. To address these two challenges, we propose a covariate-regulated sparse subspace learning (CSSL) model. We consider the scenario that these process variables lie in multiple subspaces, and only process variables from the same subspace are cross-correlated with each other. To take into account the effect of the exogenous covariates on the subspace structure, we partition the domain of the covariates into a number of regions. In each region, the subspace structure is treated as constant and can be learned independently. An efficient decision-tree-based algorithm is then proposed to obtain the solution. The proposed method can be further applied to process monitoring and fault isolation for multivariate processes. The efficacy of this method is demonstrated by comprehensive simulations and a case study on a dataset from the supervisory control and data acquisition (SCADA) system of the wind turbine.