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

• A novel procedure for diagnosing and troubleshooting plant-wide anomalies is proposed using dynamic embedded latent feature analysis. • Composite loadings are proposed for root cause analysis of an interested feature that improves the traditional bi-plots of loadings. • A dynamic inner canonical analysis algorithm with exogenous variables is proposed, which removes the impact of uninterested features. • A thorough application to an industrial plant with datasets before and after maintenance is presented to illustrate the proposed troubleshooting method. 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. [ABSTRACT FROM AUTHOR]