Dynamic data feature engineering for process operation troubleshooting

S. Joe QIN, Yingxiang LIU, Yining DONG

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

Abstract

Sustained dynamic variations in process data are usually indicators of poor control performance or malfunctioning of control valves. In this chapter, dynamically evolving latent feature analysis (DELFA) is introduced to extract interesting latent dynamic features for troubleshooting and diagnosing plant-wide process anomalies. Composite loadings and composite weights are derived and applied for diagnosing the root cause of a dynamic feature contained in one or more latent variables. The DELFA procedure is applied to an industrial plant dataset to troubleshoot process anomalies and diagnose the causes in a plant-wide setting. Dynamic inner canonical correlation analysis is shown to be superior to slow feature analysis in extracting clean dynamic features and diagnosing the root causes.
Original languageEnglish
Title of host publicationArtificial Intelligence in Manufacturing : Applications and Case Studies
EditorsMasoud SOROUSH, Richard D. BRAATZ
PublisherAcademic Press
Chapter9
Pages273-298
ISBN (Print)9780323991353
DOIs
Publication statusPublished - 2024

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