Data-driven condition-based maintenance for LNG transport systems via pipeline
| dc.contributor.author | REDJIL Ali. | |
| dc.date.accessioned | 2025-10-07T08:27:28Z | |
| dc.date.available | 2025-10-07T08:27:28Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This work presents an integrated approach to Condition-Based Maintenance (CBM) for natural gas pipelines, focusing on the Hassi R’Mel network in Algeria. Combining Bowtie analysis, interval Principal Component Analysis (PCA), and finite element simulation using ABAQUS, the study offers a comprehensive framework for failure prevention. Bowtie analysis identifies critical causes and consequences of faults; interval PCA detects anomalies from pressure, temperature, and flow data; and finite element analysis models the mechanical behaviour of corroded sections under overpressure. Results highlight the importance of continuous pressure monitoring and material integrity assessment. This methodology enables early fault detection, informed decision-making, and improved safety in pipeline operations, significantly reducing the risk of unexpected failures. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/9609 | |
| dc.language.iso | en | |
| dc.title | Data-driven condition-based maintenance for LNG transport systems via pipeline | |
| dc.type | Thesis |
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