Risk and reliability analysis for maritime autonomous surface ship: A bibliometric review of literature from 2015 to 2022

Li, Zhihong; Zhang, Di; Han, Bing; Wan, Chengpeng

doi:10.1016/j.aap.2023.107090

Cited by 14 publications

(10 citation statements)

References 100 publications

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“…Moreover, maintainability analysis carried out at the design stage of products or platforms such ships or aircraft and other complex machinery are carried out with the use of reliability analysis tools such as FTA, DFTA, BBN, etc. [8,12]. An overview of the adopted tools in this research is given.…”

Section: System Reliability Analysismentioning

confidence: 99%

“…A two-layer feedforward network with sigmoid activation and SoftMax output neurons were adopted for the study based on Equation (8). The sigmoid activation function, Equation ( 9), helps to improve the prediction capability of the neurons by adding bias and non-linearity while the SoftMax activation function, Equation (10), is a probability function with values between 0 and 1, that determines the similarity of the output to the input class.…”

Section: Feedforward Neural Networkmentioning

confidence: 99%

“…In this regard, operators are burdened with substantial maintenance expenses and frequently find themselves in forced marriages or alliances with equipment producers, in some cases having to bear the burden of equipment upgrades and retrofits due to regulations [7]. However, perception of maintenance activities differs among various operators, with many considering it to be a routine task that is only given significant attention in the case of a breakdown or when preparing for operations or exercises [8]. This approach contradicts the fundamental purpose of maintenance in that maintenance can be described as a comprehensive set of activities encompassing technical, administrative, and managerial measures undertaken throughout the lifespan of an item with the aim of preserving or reinstating its ability to fulfil the desired function [9].…”

Section: Introductionmentioning

confidence: 99%

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Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Daya,

Lazakis

2024

Machines

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Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies.

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Section: System Reliability Analysismentioning

confidence: 99%

Section: Feedforward Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Daya,

Lazakis

2024

Machines

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“…A few studies have been done in the specific field of autonomous and unmanned ships (Eriksen et al, 2021). The MUNIN project was the starting point in the literature reviewing the risk and reliability of MASS (Li et al, 2023).…”

Section: Reliabilitymentioning

confidence: 99%

“…The reliable design and the risk evaluation of mass is critical to ensure safe operations (Li et al, 2023). The lack of reliability data and the difficulty of testing remain significant obstacles when considering reliability and maintenance of machinery equipment for autonomous ships (Li et al, 2023).…”

Section: Reliabilitymentioning

confidence: 99%

Factors Influencing the Business Case for Autonomous Ships

Anemogianni-Sinanidi

EPiC Series in Technology

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Autonomous ships during any transition from conventional vessels may embrace either full or partial autonomy. At present, it is unclear how routine maintenance or breakdown, performed currently at sea, and periodic maintenance and survey, currently undertaken in port, will be scheduled. Along with these considerations matters of safety, security, reliability, availability, and the ability to remotely diagnose potential and actual failure situations will also influence the business case for autonomous ship procurement. While the initial cost of acquiring an autonomous ship is of significance in decision making, this paper principally focusses on various other aspects of the business case. Our research has shown the importance of maintenance in developing the business case for both coastal and deep-sea shipping. Indeed, maintenance and the availability of the ship will be the determining factors in whether autonomous unmanned operations will yield a profitable business case which is attractive to ship owners. Resilience during operation is a further factor that affects profitability, and this is analysed in the context of reliability and security. This paper addresses through discussion, and the results of research, into the factors influencing the business case relating to the acquisition of autonomous ships for coastal and deep-sea ships and further presents recommendations to consider when building a business case for autonomous ships.

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Contemplating Maritime Autonomous Surface Ships (MASS) under the international law on ship-source pollution

Xing

2024

Marine Pollution Bulletin

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Risk and reliability analysis for maritime autonomous surface ship: A bibliometric review of literature from 2015 to 2022

Cited by 14 publications

References 100 publications

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Factors Influencing the Business Case for Autonomous Ships

Contemplating Maritime Autonomous Surface Ships (MASS) under the international law on ship-source pollution

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