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Open AccessArticle

Importance Analysis of Components of a Multi-Operational-State Power System Using Fault Tree Models

Department of Machine Construction and Materials, Faculty of Marine Engineering, Maritime University of Szczecin, ul. Willowa 2, 71-650 Szczecin, Poland
Information 2020, 11(1), 29; https://doi.org/10.3390/info11010029
Received: 23 November 2019 / Revised: 1 January 2020 / Accepted: 1 January 2020 / Published: 5 January 2020
This article describes a case study using a fault tree analysis for a multi-operational-state system (system with several operational states) model with many different technical solutions for the power system of a fishing vessel. We describe the essence of system dependability metamodeling. A vector of external events was used to construct a detailed metamodel, depending on the operational status being modeled. In a fault tree, individual external events modify the structure of a system. The analysis includes the following operational states: sea voyages of a vessel, hauling in and paying out nets, trawling, staying in a port, and heaving to. For each operational state and assumed system configurations, the importance of system components was determined by calculating the Vesely–Fussell measures. The most important components for each operational state of a system were determined, and the critical system components, that is, those that are important in every operational state and system configuration, were identified. View Full-Text
Keywords: fault tree analysis; importance analysis; multi-operational-state system; dependability metamodeling; fishing vessel; marine system; power plant system; state vector; external events vector; operational state fault tree analysis; importance analysis; multi-operational-state system; dependability metamodeling; fishing vessel; marine system; power plant system; state vector; external events vector; operational state
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Chybowski, L. Importance Analysis of Components of a Multi-Operational-State Power System Using Fault Tree Models. Information 2020, 11, 29.

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