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

• Published in: Control engineering practice Vol. 49; pp. 149 - 158
• Main Authors: Chioua, Moncef, Bauer, Margret, Chen, Su-Liang, Schlake, Jan C., Sand, Guido, Schmidt, Werner, Thornhill, Nina F.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2016
• Subjects: Control performance monitoring; Disturbances; Fault diagnosis; Faults; Indicators; Industrial plants; Key performance indicators; Paper machine; Paper machines; Plant-wide disturbances; Principal component analysis; Root cause analysis; Spectra; Statistics; Fault diagnosis; Control performance monitoring; Paper machine; Plant-wide disturbances; Key performance indicators; Principal component analysis
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2015.10.011

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 T2 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. •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.