Realtime Data-driven Fault Detection and Diagnosis System for a Local Refinery
| Author | Mohammed, Abdulrahim | |
|---|---|---|
| metadata.dc.contributor.department | """University of Bahrain, College of Science, Department of Postgraduate Programs | |
| metadata.dc.date.accessioned | 2026-09-20T15:47:06Z | |
| Publication date | 2022 | |
| Degree level | Master | |
| Abstract | Abstract: Mechanical assets are vital for process industries business operations. Failure of these assets leads to business interruption and significant losses. Early detection of anomalies can help industries to formulate a maintenance plan and avoid unplanned downtimes. This also enhances assets’ reliability, maintainability, and availability. The aim of this study is to leverage different data-driven multivariate statistical techniques to detect a heat exchanger leak at a local refinery in real-time. The data is collected from the refinery’s historian for a heat exchanger in a distillation unit preheat train network. A set of offline models are developed using static Principal Component Analysis (PCA), dynamic PCA, static Projection to Latent Structures (PLS), and dynamic PLS to detect a heat exchanger leak that occurred in the past. The models then are deployed online to monitor the heat exchanger’s performance and detect leaks in real-time. The results of the study demonstrated that static PCA, dynamic PCA, and dynamic PLS succeeded to detect the leak 50 days (with 95% confidence limit) and 19 days (with 99% confidence limit) before it was found in the facility by operators. The online models also, without generating any false alarms, performed well in monitoring the heat exchanger’s performance in real-time. | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/id/7167bca5-38d9-4c65-ac6b-358b4b8549ac | |
| metadata.dc.identifier.other | Drupal node 2940 | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/en/dar/realtime-data-driven-fault-detection-and-diagnosis-system-local-refinery | |
| metadata.dc.identifier.uri | http://165.227.56.122/handle/123456789/6526 | |
| Language | en | |
| Link | https://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2025-03/a.rahim-thesis-report_f.pdf | |
| Title | Realtime Data-driven Fault Detection and Diagnosis System for a Local Refinery | |
| Type | Master's Thesis | |
| College | College of Science | |
| Department | Postgraduate Programs |
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