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

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

Big data analytics and machine learning of harbour craft vessels to achieve fuel efficiency : a review

The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime industry with the use of innovation and digital technologies as well as intelligent systems. The digitization of the shipping industry not only provides a competitive edge to the shipping business model but also enhances ship operational and energy efficiency. This review paper focuses on the big data analytics and machine learning applied to harbour craft vessels with the aim to achieve fuel efficiency. The paper reviews the telemetry system requires for the digitalization of harbour craft vessels, its challenges in installation, the vessel monitoring and data transmission system. The commonly used methods for data cleaning are also presented. Last but not least, the paper considers two types of the machine learning systems, i.e., supervised and unsupervised machine learning systems. The multi-linear regression and hidden Markov model for supervised machine learning system and the artificial neural network, grey box model and long short-term memory model for unsupervised machine learning are discussed, and their pros and cons are presented