Investigating ship system performance degradation and failure criticality using FMECA and artificial neural networks
Daya, A. A. and Lazakis, I.; (2022) Investigating ship system performance degradation and failure criticality using FMECA and artificial neural networks. In: 6th International Conference on Maritime Technology and Engineering (MARTECH). Instituto Superior Técnico, PRT, pp. 1-11.
Preview |
Text.
Filename: Daya_Lazakis_MARTECH_2022_Investigating_ship_system_performance_degradation_and_failure_criticality.pdf
Accepted Author Manuscript License: Strathprints license 1.0 Download (1MB)| Preview |
Abstract
The goal of all maintenance methods is to eliminate failures or reduce their occurrence. Ex-tended downtime on key ships systems such as power generation plants can lead to undesirable consequences beyond economic and operational losses, especially considering naval vessels. One solution to overcome this challenge is through a system-specific analysis that identifies the most critical component and possible causes of delays be it technical or logistics. In this regard, this paper presents a methodology using FMECA approach that adopts the risk priority number differently to identify Mission Critical Components. This was supported with ANN classification using unsupervised learning to identify patterns in the data that signifies the onset of performance degradation and potential failures onboard an OPV. The study has identified some critical components and failure patterns that contribute to extended downtime based on survey and machinery maintenance reports. Recommendations were provided on preventing/mitigating the failures and how to prioritize existing ship systems maintenance.
ORCID iDs
Daya, A. A. and Lazakis, I. ORCID: https://orcid.org/0000-0002-6130-9410;-
-
Item type: Book Section ID code: 79887 Dates: DateEvent26 May 2022Published2 March 2022AcceptedSubjects: Naval Science > Naval architecture. Shipbuilding. Marine engineering Department: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 15 Mar 2022 13:27 Last modified: 14 Dec 2024 01:07 URI: https://strathprints.strath.ac.uk/id/eprint/79887