Ship fuel consumption monitoring and fault detection via partial least squares and control charts of navigation data

Capezza, Christian; Coleman, SY; Lepore, Antonio; Palumbo, Biagio; Vitiello, Luigi

doi:10.1016/j.trd.2018.11.009

Cited by 35 publications

(17 citation statements)

References 10 publications

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“…This is related to the regulations issued by the International Maritime Organization (IMO), which focuses on greenhouse gas (GHG) emissions on ships. Capezza et al [4] conducted research related to ship fuel consumption monitoring and fault detection related to C02 emissions on the Grimaldi Group's Ro-Ro Pax cruise ship using a statistical approach method by utilizing navigation data obtained from the multi¬ sensor system installed on the ship. The method used in this study has been shown to be capable of supporting fault detection and verifying the C02 savings achieved after energy efficiency initiatives.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fuel Consumption Monitoring System Integrated With Internet Of Things (IOT)

Hapsari

Prastowo

Pitana

2021

Kapal

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Fuel is an important aspect in the operation of ships that require high costs. The high cost of fuel is not followed by an automatic fuel monitoring process. By not using the fuel consumption monitoring method that works automatically, the shipping management does not know for sure the ship's fuel consumption is in accordance with the shipping mileage, thus triggering fraud committed by the ship's crew against the ship's fuel. Fuel consumption monitoring is carried out primarily to identify opportunities to improve energy efficiency and reduce costs. By following technological developments, Internet of Things (IoT) technology has begun to be applied in various industrial fields because it can transmit data in real-time via the internet network without human-to-human or human-to-computer interaction. In this research, the design of models and experiments related to a monitoring system for fuel consumption was carried out using sensors and microcontrollers integrated with the internet to obtain accurate and real-time data. The test results show that the volume of fuel available in the tank, the volume of fuel discharged, the flow rate of fuel, and the location of the system can be known by the user in real-time via the IoT website. Based on the results of measurements using an ultrasonic level sensor, it is known that the measurement results are quite accurate with a deviation of ± 0.5 cm. Meanwhile, the measurement results by the flow sensor are less accurate because the fuel flow only relies on the force of gravity.

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Section: Introductionmentioning

confidence: 99%

Real-Time Fuel Consumption Monitoring System Integrated With Internet Of Things (IOT)

Hapsari

Prastowo

Pitana

2021

Kapal

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“…Following the above, the current literature on maritime FD is not as widespread as in other industrial sectors. For example, Capezza et al (2019) investigated the use of partial least squares regression in combination with the Hotelling's T 2 and the squared prediction error control charts, for FD under the scope of emissions monitoring. Also, the benefits of predictive maintenance have been addressed under a decision support framework using fuzzy-sets enhanced with Analytical Hierarchy Process (AHP) (Lazakis and Ölçer, 2015).…”

Section: Fault Detection In the Maritime Industrymentioning

confidence: 99%

Machine learning and data-driven fault detection for ship systems operations

2020

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Well maintained vessels exhibit high reliability, safety and energy efficiency. Even though machinery failures are inevitable, their occurrence can be foreseen when predictive maintenance schemes are implemented. Predictive maintenance may be optimally applied through condition, performance, and process monitoring. Most importantly, it can include the detection of developing faults, which affect the performance of ship systems and hinder energyefficient operations of ships. Under this viewpoint, this paper proposes a new data-driven fault detection methodology in a novel application for shipboard systems, by exploring the "learning potential" of recorded voyage data. The proposed methodology, combines the benefits of Expected Behaviour (EB) models, by selecting the optimal regression model, with the Exponentially Weighted Moving Average (EWMA) for fault detection, in novel ship applications. It is seen that a multiple polynomial ridge regression model, with testing score of nearly 0.96 and can accurately detect certain developing faults manifesting in both the Main Engine (ME) cylinder Exhaust Gas (EG) temperature and the ME scavenging air pressure. The early detection of developing faults can be used to supplement the daily monitoring of ship operations and enable the planning of pre-emptive rectifying actions by reducing sub-optimal machinery conditions.

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“…e energy management (EM) system from Rolls Royce offers decision-making approach to reduce fuel consumption and operating costs [21]. Based on the collected data, Capezza et al [22] used statistical methods to analyze abnormality and used the partial least square algorithm to do fuel consumption regression. e aforementioned systems can assist in decision support and assess ship performance trends.…”

Section: Introductionmentioning

confidence: 99%

A Novel Big Data Collection System for Ship Energy Efficiency Monitoring and Analysis Based on BeiDou System

Zeng

Chen

2021

Journal of Advanced Transportation

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The call for green shipping is increasing, and the reduction of greenhouse gas emissions from ships becomes more and more important. Traditional ship energy efficiency monitoring is based on the noon reports, which are susceptible to human error and have a time delay. Many ship energy efficiency monitoring systems have been designed and developed, but they usually cannot send data to the shore in time. In order to identify abnormal fuel consumption in time, this paper realizes a big data collection system for ship energy efficiency monitoring based on the BeiDou System. The system installed on two sister container ships has already collected a lot of data. Big data analysis methods, such as principal component analysis (PCA) and correlation analysis, are applied in the system to realize data visualization and analysis. Using PCA, it turns out that the shaft power of the main engine is related to a certain ship speed, which is also affected by load and weather conditions, and is the biggest factor in determining fuel consumption. To realize the assessment of hull fouling and the optimization of ship trim, a useful physics-based analysis is proposed. The analysis shows that the fouling of ship body greatly increases its resistance. Our analysis method can also find the best trim under specific loading condition. All these points are important for reducing fuel consumption and improving ship efficiency.

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Ship fuel consumption monitoring and fault detection via partial least squares and control charts of navigation data

Cited by 35 publications

References 10 publications

Real-Time Fuel Consumption Monitoring System Integrated With Internet Of Things (IOT)

Real-Time Fuel Consumption Monitoring System Integrated With Internet Of Things (IOT)

Machine learning and data-driven fault detection for ship systems operations

A Novel Big Data Collection System for Ship Energy Efficiency Monitoring and Analysis Based on BeiDou System

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