The Temperature Forecast of Ship Propulsion Devices from Sensor Data

Li, Taoying; Hua, Miao; Yin, Qian

doi:10.3390/info10100316

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…One of the purposes of this type of monitoring system is to analyze engine fuel oil consumption to detect engine wear. Another neural network application in the marine field is due to [85], which proposed a process to forecast the temperature of the ship propulsion devices in order to detect damages by giving an alert automatically. Autonomous shipping operations have been under investigation in recent years due to the possibility of using them in critical situations unreachable by humans.…”

Section: Sensor Application For Navigation and Onboard Securitymentioning

confidence: 99%

Review on Sensors for Sustainable and Safe Maritime Mobility

Briguglio,

Crupi

2024

JMSE

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The increasingly stringent requirements—in terms of limiting pollutants and the constant need to make maritime transport safer—generated the necessity to foresee different solutions that are original. According to the European Maritime Safety Agency, the total number of reported marine casualties and incidents is 21.173 over the period 2014–2021, with a yearly average of 2.647 casualties and incidents. According to the same source, 495 cases of pollution were reported in the period from 2014 to 2021. Marine pollution by ships’ fuel and other pollutants is linked to 64.2% of all pollution. It is mandatory to offer a new kind of ship that can exploit new technologies to increase safety for people and onboard goods. It has been found that existing marine structures for maritime mobility do not have essential sensors for avoiding emergency situations such as flooding, oil spills, or health situations requiring efficient monitoring. In addition, there is a lack of legislation defining the insertion of unmanned vehicles into the actual marine infrastructure. This review highlighted the strengths and weaknesses of sensors in the maritime sector, intensifying areas of improvement for future challenges, such as sensor energy efficiency, data processing, sensor fusion methodologies, and accurate sea state description with environmental monitoring by means of unmanned vehicles.

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Section: Sensor Application For Navigation and Onboard Securitymentioning

confidence: 99%

Review on Sensors for Sustainable and Safe Maritime Mobility

Briguglio,

Crupi

2024

JMSE

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“…In the state of the art different PdM techniques using LSTM after an autoencoder to predict malfunctioning components or assets have been implemented on data from temperature sensors, flowmeters, pressure and speed sensors in industrial machinery [44][45][46][47][48] and based on data vibration [49]. However, it is necessary to highlight, at this point, the efforts of the academy to advance in the knowledge with respect to the application of unsupervised techniques on naval machinery as propulsion devices [50][51][52]. Some of the available ML-based studies focus on optimizing the energy consumption of propulsion plants [53,54].…”

Section: State Of the Artmentioning

confidence: 99%

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

et al. 2021

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Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

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“…At present, as an important carrier of world trade, shipping is an important driving force for economic globalization. 1 The physical condition of ships and their systems and machinery is one of the factors that affect shipping performance. 2 With the continuous development of ship intelligent technology, the probability of failure of the main equipment of ships is increasing.…”

Section: Introductionmentioning

confidence: 99%

Research on fault prediction of marine diesel engine based on attention-LSTM

Liu

Gan

Cong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

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The marine diesel engine is an important guarantee for the safe operation of the ship. It is of great significance to predict the running trend of the diesel engine and infer whether the diesel engine is in an abnormal working state in advance to ensure the safe operation of the ship. In this paper, combining the feature extraction ability of the attention mechanism (Attention) and the time-series memory ability of the long short-term neural network (LSTM), a prediction model of the exhaust gas temperature of the marine diesel engine is constructed. Meanwhile, in order to improve the prediction accuracy of the model, the improved particle swarm algorithm is used to optimize the structural parameters of the model, and a model with higher prediction performance is obtained. Then, according to the residual value distribution between the predicted value and the actual value of the model, the process control method is used to set the fault threshold, and the experimental data of normal and abnormal conditions are used for verification. The results show that the proposed model can accurately predict developing failures and provide a new method for ship condition maintenance.

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The Temperature Forecast of Ship Propulsion Devices from Sensor Data

Cited by 4 publications

References 21 publications

Review on Sensors for Sustainable and Safe Maritime Mobility

Review on Sensors for Sustainable and Safe Maritime Mobility

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

Research on fault prediction of marine diesel engine based on attention-LSTM

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