Design of a decision support system to achieve condition-based maintenance in ship machinery systems

Karatuğ, Çağlar; Arslanoğlu, Yasin; Soares, C. Guedes

doi:10.1016/j.oceaneng.2023.114611

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…In accordance with Stopford's [3] findings, it is generally observed that ships are constructed with an intended operational lifespan of approximately 25 years, with the potential for extension up to 35 years or beyond [4]. The life cycle management of ships and on-board systems is frequently not adequately prioritised, resulting in significant maintenance costs beyond the initial 5-year period [5,6].…”

Section: Introductionmentioning

confidence: 83%

“…Nonetheless, the application of data-driven methods in predictive maintenance techniques were widely researched by authors such as Li, Gebraeel [18,19] and Wang, Chen [16], providing efficient optimisation models for implementation. Similarly, the use of supervised and non-supervised learning for clustering and anomaly detection-based machinery performance degradation on naval propulsion system was presented in [20,21].The use of data driven methods in machinery reliability and degradation analysis have been research in [6,22] where the authors have developed methodologies to improve reliability analysis outcomes using data-driven methods, especially in machinery performance degradation analysis.…”

Section: Critical Literature Reviewmentioning

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: Introductionmentioning

confidence: 83%

Section: Critical Literature Reviewmentioning

confidence: 99%

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

Daya,

Lazakis

2024

Machines

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“…Tugboats have a design that is not too big but must be able to be arranged so that equipment, especially machinery, can enter and gain access easily (Özhan Doğan, 2023). Basically, the electrical system on a ship is the main factor of several important elements that support operational activities (Karatuğ et al, 2023;Sarıalioğlu et al, 2020). Therefore, if a disturbance occurs, it is feared that the ship will not be able to operate optimally.…”

Section: Introductionmentioning

confidence: 99%

Design of Rotary Motor Wiring Diagram for Tugboat Ship Maneuvering System Using Cam Switch ControlDesign of Rotary Motor Wiring Diagram for Tugboat Ship Maneuvering System Using Cam Switch Control

Sugeng,

Khristyson,

Panjalu

2024

ETJ

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The maneuverability of tugboats is very necessary for narrow turning areas, so many tugboat maneuvering systems use motors which are currently applied with cam switches. It is important to plan a wiring system to anticipate disruptions to distribution electricity. Therefore, the electricity supply for the equipment can be met and run as expected. The aim of this research is to obtain rotary motor wiring diagram system for tugboat ship maneuvering system using cam switch control. The R&D (Research and Development) method combined with experiments was carried out to obtain an ideal rotary motor wiring diagram system based on the dimensions of the tugboat's engine room. In forward rotation for this motor is a decrease before stabilization occurs, the voltage is 18% meanwhile, reverse rotation experienced a decrease of 32%. This difference with motor if workload which is charged which also experiences variations, for forward rotation the increase is 48%, also for reverse rotation it is 43%.

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“…Furthermore, neural network (NN) methods have found application in diverse maritime domains, encompassing ship resistance prediction [9][10][11][12], ship engine power [13,14] and performance forecasting [15][16][17], ship hydrostatics [18], ship hydrodynamics, and motion prediction [19][20][21], as well as condition-based maintenance of machinery systems [22] and fault diagnosis [23]. The effectiveness of regression methods in accurately estimating both point and range estimates for parameters has been well-documented through research across various disciplines of science [24,25].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

Karaçay,

Karatuğ,

Uyanık

et al. 2024

Applied Sciences

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Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.

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Design of a decision support system to achieve condition-based maintenance in ship machinery systems

Cited by 13 publications

References 36 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

Design of Rotary Motor Wiring Diagram for Tugboat Ship Maneuvering System Using Cam Switch ControlDesign of Rotary Motor Wiring Diagram for Tugboat Ship Maneuvering System Using Cam Switch Control

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

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