Sciences des données et diagnostic prédictif dessystèmes de mesure dans l'industrie 4.0
| dc.contributor.author | LAKEHAL AYMEN Enc: CHAKOUR Chouaib | |
| dc.date.accessioned | 2026-07-14T07:35:23Z | |
| dc.date.available | 2026-07-14T07:35:23Z | |
| dc.date.issued | 2026-07-04 | |
| dc.description.abstract | In the context of Industry 4.0, the increasing number of sensors and the inherent uncertainty of industrial measurements make predictive fault diagnosis increasingly challenging. This thesis addresses the diagnosis of industrial systems using Principal Component Analysis (PCA), a well-established statistical method for modeling highly correlated processes. After reviewing the theoretical foundations of classical PCA and its limitations when dealing with imprecise data, this work proposes an extension to interval-valued data through the SPCA (Symbolic Principal Component Analysis) approach, allowing for a more realistic representation of measurement uncertainty. This approach is then applied to fault detection and isolation, using respectively the SPE index and the variable reconstruction principle, both extended to the symbolic framework. The performance of the proposed method is validated through an application to a real industrial case : a benchmark dataset from a gas turbine. The results show that the SPCA approach enables reliable fault detection and accurate fault isolation, even under measurement uncertainty, confirming its relevance for the monitoring and predictive diagnosis of modern industrial systems. | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/6112 | |
| dc.language.iso | fr | |
| dc.publisher | Université 20 Août 1955 - Skikda Faculté de Technologie Département de génie électrique | |
| dc.title | Sciences des données et diagnostic prédictif dessystèmes de mesure dans l'industrie 4.0 | |
| dc.title.alternative | Instrumentation | |
| dc.type | Thesis |