Component criticality analysis for improved ship machinery reliability

Daya, Abdullahi Abdulkarim and Lazakis, Iraklis (2023) Component criticality analysis for improved ship machinery reliability. Machines, 11 (7). 737. ISSN 2075-1702 (https://doi.org/10.3390/machines11070737)

[thumbnail of Daya-lazakis-Machines-2023-Component-criticality-analysis-for-improved-ship-machinery-reliability]
Preview
 Text. Filename: Daya_lazakis_Machines_2023_Component_criticality_analysis_for_improved_ship_machinery_reliability.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

Abstract

Redundancy in ship systems is provided to ensure operational resilience through equipment backups, which ensure system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems; hence, it is provided with a good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired blackouts is common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensuring a more robust maintenance and support platform for efficient ship operations. In this regard, a hybrid reliability and fault detection analysis using DFTA and ANN was conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of four marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate an overall low system reliability of less than 70 percent within the first 24 of the 78 operational months. Component criticality-using reliability importance measures obtained through DFTA was used to identify all components with more than a 40 percent contribution to subsystem failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Network classification. The ANN has identified a failure pattern threshold at about 200 kva, which can be attributed to overheating, hence establishing a link between component failure and generator performance.

ORCID iDs

Daya, Abdullahi Abdulkarim and Lazakis, Iraklis ORCID logoORCID: https://orcid.org/0000-0002-6130-9410;
CORE (COnnecting REpositories)