Plant-wide troubleshooting and diagnosis using dynamic embedded latent feature analysis

Abstract

Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anomalies using dynamic embedded latent feature analysis (DELFA). To remove the impact of external disturbances or exogenous variables, a dynamic inner canonical correlation analysis algorithm with exogenous variables is proposed. Composite loadings and composite weights are derived and applied for diagnosing a feature that is contained in several latent variables. The dynamic embedded latent features are usually related to poor control performance or malfunctioning control instrumentation. The proposed DELFA procedure with dynamic latent scores and composite loadings is applied to two industrial datasets of a chemical plant before and after a troubled control valve was fixed. The case study demonstrates convincingly that latent dynamic features are powerful for troubleshooting of process anomalies and diagnosing their causes in a plant-wide setting.