A new ship safety management approach - learning from the past, managing future risks

Learning from the past has been recognised as an effective means to manage future challenges. This is particularly true for ship safety management in the maritime industry as the records of historical safety-related failures are generally accompanied by the losses of human lives, damage to the environment and the ships. However, the current 'learning' practice is not rationalised to facilitate effective safety management both from design and operational points of view. By proposing a unique approach of 'learning from the past', this paper elaborates on a formal methodology towards ship safety management so that future risk control decisions can be made in an objective, transparent, and well-informed manner

Improvement of ship stability and safety in damaged condition through operational measures : challenges and opportunities

Guaranteeing a sufficient level of safety from the point of view of stability is typically considered to be a matter of design. The overall level of safety of a ship, however, can only be guaranteed when considering passive design measures in conjunction with active operational measures, in a holistic, balanced and cost-effective manner. Time could therefore be coming for systematically considering operational measures as a recognised and normed integral part of a holistic approach to ship safety from the point of view of stability. In this respect, the scope of this paper is to identify open challenges and to provide, in general, food for thought for stimulating a discussion on the topic of operational measures, with specific attention to the damaged ship condition. The aim is to provide ground for further proceeding towards the goal of implementing a virtuous integrated approach to ship stability safety which gives due credit to effective and robust operational risk control options

Ship stability & safety in damage condition through operational measures

Guaranteeing a sufficient level of safety from the point of view of stability is typically considered to be a matter of design. However, it is impossible to ensure safety only by design measures, and operational measures can then represent a complementary tool for efficiently and cost-effectively increasing the overall safety of the vessel. Time could therefore be coming for systematically considering operational measures as a recognised and normed integral part of a holistic approach to ship safety from the point of view of stability. In this respect, the scope of this paper is to identify open challenges and to provide, in general, food for thought for stimulating a discussion on the topic of operational measures, with specific attention to the damaged ship condition. The aim of the discussion should be to provide ground for further proceeding towards the goal of implementing a virtuous integrated approach to ship stability safety which gives due credit to effective and robust operational risk control options

Sustainable energy propulsion system for sea transport to achieve United Nations sustainable development goals : a review

The cost of renewable energy technologies such as wind and solar is falling significantly over the decade and this can have a large influence on the efforts to reach sustainability. With the shipping industry contributing to a whopping 3.3% in global CO2 emissions, the International Maritime Organization has adopted short-term measures to reduce the carbon intensity of all ships by 50% by 2050. One of the means to achieve this ambitious target is the utilisation of propulsion systems powered by sustainable energy. This review paper summarises the current state of the adoption of renewable energy and alternative fuels used for ship propulsion. Special focus is given to the means of these alternative energies in achieving the United Nations Sustainable Development Goals, in particular Goal 7 (Affordable and Clean Energy), Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). A state-of-the-art for various ships powered by renewable energy and alternative fuels is investigated and their technologies for mitigating carbon emissions are described. The cost for each technology found in the literature is summarised and the pros and cons of each technology are studied

Achieving fuel efficiency of harbour craft vessel via combined time-series and classification machine learning model with operational data

This paper presents work on forecasting the fuel consumption rate of a harbour craft vessel through the combined time-series and classification prediction modelling. This study utilizes the machine learning tool which is trained using the 5-month raw operational data, i.e., fuel rate, vessel position and wind data. The Haar wavelet transform filters the noisy readings in the fuel flow rate data. Wind data are transformed into wind effect (drag), and the vessel speed is acquired through transforming GPS coordinates of vessel location to vessel distance travelled over time. Subsequently, the k -means clustering groups the tugboat operational data from the same operations (i.e., cruising and towing) for the training of the classification model. Both the time-series (LSTM network) and classification models are executed in parallel to make prediction results. The comparison of empirical results is made to discuss the effect of different architectures and hyperparameters on the prediction performance. Finally, fuel usage optimization by hypothetical adjustment of vessel speed is presented as one direct application of the methods presented in this paper

Self-labelling of tugboat operation using unsupervised machine learning and intensity indicator

The actual operational data, such as a time sequence of fuel consumption and speed, is usually unlabeled or not associated with a specific activity like tugging or cruising. The operation type is critical for further analysis, as tugging and cruising operations require different fuel and navigation profiles. This paper aims to develop a self-labelling framework for tugboat operation by using unsupervised machine learning and a proposed intensity indicator. The framework considers two sets of data: the positional data and the fuel consumption rate data. The fuel consumption data is obtained from mass flowmeters installed on tugboats, while the positional data are navigational data purchased from marine data aggregators. The developed self-labelling enables ship operators in identifying operations and locations that require heavy fuel consumption andcan be used for further big data analytics and machine learning for fuel consumption prediction when vessel speeds are known

Filtering harbor craft vessels' fuel data using statistical, decomposition, and predictive methodologies

Filtering is the process of defining, recognizing, and correcting flaws in given data so that the influence of inaccuracies in input data on subsequent studies is minimized. This paper aims to discuss the characteristics of some filtering methods from various topics. Wavelet transform and frequency (Fourier) transform are considered for the decomposition methodologies whereas descriptive statistics is used for the statistical methodology. The Kalman filter and autoencoder neural network are also explored for the predictive methodologies. All the aforementioned methodologies are discussed empirically using two metrics of R-squared and mean absolute error. This paper aims to study the effectiveness of these filtering techniques in filtering noisy data collected from mass flowmeter reading in an unconventional situation i.e., on a tugboat while in operation to measure fuel consumption. Finally, the performance of various filtering methods is assessed, and their effectiveness in filtering noisy data is compared and discussed. It is found that the Haar wavelet transforms, Kalman filter and the descriptive statistics have a better performance as compared to their counterparts in filtering out spikes found in the mass flow data

Sustainable hybrid marine power systems for power management optimisation : a review

The increasing environmental concerns due to emissions from the shipping industry have accelerated the interest in developing sustainable energy sources and alternatives to traditional hydrocarbon fuel sources to reduce carbon emissions. Predominantly, a hybrid power system is used via a combination of alternative energy sources with hydrocarbon fuel due to the relatively small energy efficiency of the former as compared to the latter. For such a hybrid system to operate efficiently, the power management on the multiple power sources has to be optimised and the power requirements for different vessel types with varying loading operation profiles have to be understood. This can be achieved by using energy management systems (EMS) or power management systems (PMS) and control methods for hybrid marine power systems. This review paper focuses on the different EMSs and control strategies adopted to optimise power management as well as reduce fuel consumption and thus the carbon emission for hybrid vessel systems. This paper first presents the different commonly used hybrid propulsion systems, i.e., diesel–mechanical, diesel–electric, fully electric and other hybrid systems. Then, a comprehensive review of the different EMSs and control method strategies is carried out, followed by a comparison of the alternative energy sources to diesel power. Finally, the gaps, challenges and future works for hybrid systems are discussed

Real-time instance segmentation for detection of underwater litter as a plastic source

Thousands of tonnes of litter enter the ocean every day, posing a significant threat to marine life and ecosystems. While floating and beach litter are often in the spotlight, about 70% of marine litter eventually sinks to the seafloor, making underwater litter the largest accumulation of marine litter that often goes undetected. Plastic debris makes up the majority of ocean litter and is a known source of microplastics in the ocean. This paper focuses on the detection of ocean plastic using neural network models. Two neural network models will be trained, i.e., YOLACT and the Mask R-CNN, for the instance segmentation of underwater litter in images. The models are trained on the TrashCAN dataset, using pre-trained model weights trained using COCO. The trained neural network could achieve a mean average precision () of 0.377 and 0.365 for the Mask R-CNN and YOLACT, respectively. The lightweight nature of YOLACT allows it to detect images at up to six times the speed of the Mask R-CNN, while only making a comparatively smaller trade-off in terms of performance. This allows for two separate applications: YOLACT for the collection of litter using autonomous underwater vehicles (AUVs) and the Mask R-CNN for surveying litter distribution