Semi-physical neural modeling for linear signal restoration

International audienceThis paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort

Analysis of uncertainties in the prediction of ships’ fuel consumption – from early design to operation conditions

This investigation presents an approach that contributes to a better understanding of achievable accuracy of fuel consumption predictions of ships and provides an example of how a thorough uncertainty analysis of prediction models can be performed. A generic ship energy systems model is used for the fuel consumption prediction of two reference ships: a RoRo ship and a tanker. The study presents how method and design uncertainties can be categorized and handled in four different phases of a ship’s life – from early design to ship operation. Monte Carlo simulations are carried out for two conditions (calm water and operation at sea) to calculate the mean (expected value) and uncertainty (standard deviation) of the fuel consumption. The results show that the uncertainty in the fuel consumption prediction in a very early phase of the design process is approximately 12%, whereas at a very late phase, it reduces to less than 4%. Finally, the simulation model and the approach to predict the fuel consumption presented in the study are applied to a real ship during operation conditions to demonstrate its features for a real case

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

Analysis of uncertainties in the prediction of ships’ fuel consumption – from early design to operation conditions

This investigation presents an approach that contributes to a better understanding of achievable accuracy of fuel consumption predictions of ships and provides an example of how a thorough uncertainty analysis of prediction models can be performed. A generic ship energy systems model is used for the fuel consumption prediction of two reference ships: a RoRo ship and a tanker. The study presents how method and design uncertainties can be categorized and handled in four different phases of a ship’s life – from early design to ship operation. Monte Carlo simulations are carried out for two conditions (calm water and operation at sea) to calculate the mean (expected value) and uncertainty (standard deviation) of the fuel consumption. The results show that the uncertainty in the fuel consumption prediction in a very early phase of the design process is approximately 12%, whereas at a very late phase, it reduces to less than 4%. Finally, the simulation model and the approach to predict the fuel consumption presented in the study are applied to a real ship during operation conditions to demonstrate its features for a real case

Rigid Body Ship Dynamics

It is common today that operational data is recorded onboard ships within the Internet of Ships (IoS) paradigm. This enables the possibility to build ship digital twins as digital copies of the real ships. Predicting the ship’s motions with ship dynamics could be an important sub-component of these ship digital twins.A model for the ship’s dynamics can be identified based on observations of the ship’s motions. The identified model will have model uncertainty due to imperfections and idealizations made in physical model formulations as well as uncertainty from errors in the measurement data, which can be very pronounced when using full scale operational data. It is easier to develop accurate models with low model uncertainty using data obtained in a controlled laboratory environment where the measurement errors are much lower, especially in calm water conditions. The prediction model should be able to describe scenarios that a ship has never encountered before, which is possible if much of the underlying physics has been identified. Grey-box modelling is a technique which combines operational data with physical principles to achieve this. The objective of this thesis is to develop system identification methods for grey box models with good generalization of the model scale rigid body ship dynamics in calm waters. A model development procedure is proposed to handle the model uncertainty through the selection of candidate models based on a hold-out evaluation procedure. The measurement noise is handled by an iterative preprocessor, which uses an extended Kalman filter (EKF) and a Rauch Tung Striebel (RTS) smoother that uses an initially estimated predictor model from semi-empirical formulas. It is demonstrated that the ship’s roll motion with high accuracy can be described using a quadratic damping model. For the more complex manoeuvring models, multicollinearity is a large problem where the appropriate complexity needs to be selected with the bias-variance trade-off between underfitting or overfitting the data. Hold-out turning circle tests were predicted with high accuracy for the wPCC and KVLCC2 test case ships with models from the proposed development procedure and parameter estimation method.The proposed methods can produce prediction models with high generalization given that a suitable model structure has been selected from the candidate models and an appropriate split in the hold-out evaluation of the model development process has been applied

Prediction and optimisation of fuel consumption for inland ships considering real-time status and environmental factors

The information about ships’ fuel consumption is critical for condition monitoring, navigation planning, energy management and intelligent decision-making. Detailed analysis, modelling and optimisation of fuel consumption can provide great support for maritime management and operation and are of significance to water transportation. In this study, the real-time status monitoring data and hydrological data of inland ships are collected by multiple sensors, and a multi-source data processing method and a calculation method for real-time fuel consumption are proposed. Considering the influence of navigational status and environmental factors, including water depth, water speed, wind speed and wind angle, the Long Short-Term Memory (LSTM) neural network is then tailored and implemented to build models for prediction of real-time fuel consumption rate. The validation experiment shows the developed model performs better than some regression models and conventional Recurrent Neural Networks (RNNs). Finally, based on the fuel consumption rate model and the speed over ground model constructed by LSTM, the Reduced Space Searching Algorithm (RSSA) is successfully used to optimise the fuel consumption and the total cost of a whole voyage

From ethnographic research to big data analytics - A case of maritime energy-efficiency optimization

The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a big data approach to achieve energy efficiency (EE). Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this interdisciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy researc

Development of speed-power performance models for ship voyage optimization

Various measures, such as voyage optimization, performance monitoring and ship cleaning schedules, have been developed to help increase the energy efficiency of shipping operations. One of the most important elements needed for these measures is a reliable ship speed-power model. Many research efforts have been devoted to developing such models to describe a ship’s energy performance for head-to-beam seas, which are important for ship design purposes. For measures to increase the energy efficiency of a ship’s operations, speed-power performance models for other heading angles are of equal importance but are rarely investigated. Therefore, the overall objective of this thesis is to develop speed-power models for arbitrary wave headings that are especially applicable for ship voyage optimization. First, a semi-empirical model is proposed based on experimental tests. Then, a machine learning model (black box) is developed based on a large amount of full-scale measurement data.For the semi-empirical model, formulas to estimate a ship’s added resistance in head waves are developed to effectively describe a ship’s hull forms and other main characteristics. The formulas are then extended to estimate the impacts of wave headings from different angles, and these are verified by experimental model tests. A significant wave height-based correction factor is proposed to consider the nonlinear effect on a ship’s resistance and power increase due to irregular waves. For the machine learning-based model, the XGBoost algorithm is used to establish the model based on full-scale measurements of a PCTC. The input features include parameters related to ship operation profiles, metocean conditions, and motion responses.For the three case study ships, the discrepancy between power predictions and the actual values is reduced from more than 40% using today’s well-recognized methods to approximately 5% using the semi-empirical model proposed in this thesis. The machine learning model can further reduce the discrepancy to less than 1%. It is also demonstrated that the improved models can help to effectively optimize a ship’s voyage planning to reduce fuel consumption

From ethnographic research to big data analytics - A case of maritime energy-efficiency optimization

The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a bigdata approach to achieve energy efficiency(EE).Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this inter disciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy research.\ua0\ua9 2020 by the authors