Plant-wide root cause identification using plant key performance indicators (KPIs) with application to a paper machine. (April 2016)

• Record Type: Journal Article
• Authors: Chioua, Moncef; Bauer, Margret; Chen, Su-Liang; Schlake, Jan C.; Sand, Guido; Schmidt, Werner; Thornhill, Nina F.
• Is Part Of: Control engineering practice. Volume 49(2016)
• Page Start: 149
• Page End: 158
• Publication Date: 2016-04
• Subjects: Plant-wide disturbances -- Control performance monitoring -- Fault diagnosis -- Principal component analysis -- Paper machine -- Key performance indicators; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2015.10.011
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Previously, plant-wide disturbance analysis has looked into the propagation of faults through an industrial production process by investigating process measurements. However, the extent of the analysis has mostly been limited to a section of a plant. In this work, we propose a top-down approach which investigates measurements of the complete plant and identifies a section where the disturbance originates. Root cause analysis is carried out thereafter to pinpoint the faulty asset. The proposed approach has three novel elements: Using key performance indicators (KPI) as reference and starting point of the analysis, restricting measurements to a measurement type (e.g. flow) thus focusing on a section and applying the novel method of contribution plots of spectral PCA T 2 statistic to find the contribution of each measurement towards the disturbance observed in the KPI. The approach is described and carried out on a paper machine where a quality KPI showed an established oscillation. Highlights: Top-down approach for root cause analysis using key performance indicators (KPI). Studied the differences between bottom-up and top-down approaches for root cause analysis. Applied top-down approach for root cause analysis to an industrial process.