Reliability assessment of marine floating structures using Bayesian network

Abaei, Mohammad Mahdi; Abbassi, Rouzbeh; Garaniya, Vikram; Chai, Shuhong; Khan, Faisal

doi:10.1016/j.apor.2018.04.004

Cited by 58 publications

(10 citation statements)

References 31 publications

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“…Different techniques for risk-based reliability modelling have been reported in the literature, among which, Bayesian statistics-based approaches and particularly Bayesian networks are of great importance. Several applications of Bayesian networks for probabilistic reliability assessment of offshore systems can be found in works of Li et al (2020) ; Abaei et al (2018a) , b and Arzaghi et al (2020) for floating offshore wind turbines, general floating offshore platforms, ship groundings and subsea pipelines, respectively.…”

Section: Reliability Analysis Of the Integrated Systemmentioning

confidence: 99%

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Aryai

Abdussamie

Salehi

et al. 2021

Process Safety and Environmental Protection

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Sustainable use of the ocean for food and energy production is an emerging area of research in different countries around the world. This goal is pursued by the Australian aquaculture, offshore engineering and renewable energy industries, research organisations and the government through the “Blue Economy Cooperative Research Centre”. To address the challenges of offshore food and energy production, leveraging the benefits of co-location, vertical integration, infrastructure and shared services, will be enabled through the development of novel Multi-Purpose Offshore-Platforms (MPOP). The structural integrity of the designed systems when being deployed in the harsh offshore environment is one of the main challenges in developing the MPOPs. Employing structural reliability analysis methods for assessing the structural safety of the novel aquaculture-MPOPs comes with different limitations. This review aims at shedding light on these limitations and discusses the current status and future directions for structural reliability analysis of a novel aquaculture-MPOP considering Australia’s unique environment. To achieve this aim, challenges which exist at different stages of reliability assessment, from data collection and uncertainty quantification to load and structural modelling and reliability analysis implementation, are discussed. Furthermore, several solutions to these challenges are proposed based on the existing knowledge in other sectors, and particularly from the offshore oil and gas industry. Based on the identified gaps in the review process, potential areas for future research are introduced to enable a safer and more reliable operation of the MPOPs.

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Section: Reliability Analysis Of the Integrated Systemmentioning

confidence: 99%

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Aryai

Abdussamie

Salehi

et al. 2021

Process Safety and Environmental Protection

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“…This presents the system's structural interaction and performance functions, with consideration of external factors such as the harsh environments. The characterizations of the identified relational parameters within the system are represented using sheet‐based, table–based, and diagram‐based qualitative reliability methods …”

Section: Literature Reviewmentioning

confidence: 99%

“…The characterizations of the identified relational parameters within the system are represented using sheet-based, table-based, and diagram-based qualitative reliability methods. 2,3,10,11 The sheet-based approach checklist is used to determine and identify failure influencing factors in design and, operations, and during maintenance. The checklist provides a set of questions for hazard identification (HAZID) for reliability engineer and system operators, as a means of gathering facts on critical contributing parameters.…”

Section: Reliability Analysis Techniques For the Offshore Energy Systemmentioning

confidence: 99%

A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Adumene

Okoro

2020

Engineering Reports

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For an effective monitoring strategy of offshore energy systems in harsh environments, it is vital the system reliability dynamics be fully understood. This article presents the application of a Markovian process for dynamic reliability prediction of an offshore energy system. In the proposed approach, a three‐state Markovian process is developed for the analysis of the subsystem states, which dynamically determines the overall system reliability performance, overcoming the static limitation of fault tree analysis. The multistate subsystems are discretized into normal, degraded, and failed states to demonstrate the performance dynamics of the system for condition monitoring. The proposed approach is tested on a case study and the most critical influencing elements are identified. The application of the methodology efficiently identifies the system vulnerability path so as to assist in system integrity management and to provide a guide to early remedial action against total failure and downtime in offshore energy generation.

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“…Rendón-Conde and Heredia-Zavoni 6 considered statistical parameter uncertainties related to the maximum dynamic tension and breaking resistance of mooring lines and developed a first-order analytic formulation to solve the reliability index. Abaei et al 7 estimated the failure probability of a tensioned mooring system using a Bayesian network (BN). Stanisic et al 8 presented the reliability analysis of the mooring system of a floating liquefied natural gas (FLNG) vessel in a tropical cyclone environment.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of mooring lines for floating structures using ANN-BN inference

Zhao

Dong

Jiang

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.

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Reliability assessment of marine floating structures using Bayesian network

Cited by 58 publications

References 31 publications

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Reliability analysis of mooring lines for floating structures using ANN-BN inference

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