A method for the reduction of ship fuel consumption through the optimisation of vessel draft, speed and trim using historical vessel data. Historical global, online data, is collected for multiple vessel operating parameters associated with its previous voyages. After initial filtering and cleaning of the gathered data, a process of analysing the data to determine the optimum draft, speed and trim for the vessels' given speed is described. The determined optimum draft, speed and trim values are then presented to the Captain or an automatic draft and trim optimisation system for the current draft and trim to be adjusted. This application therefore discloses a method for analysing historical vessel data to provide advice on optimum draft, trim and speed. A method for predicting the achievable fuel savings and recording the fuel savings achieved is also disclosed.

The disclosed method measures a line angle of a mooring line connected to a floating object floating in a body of water, the mooring line being connected between the floating object and an anchoring body disposed in a bed of the body of water, in which the floating object is coupled to the mooring line by a line connector. The method includes: defining at least three data points each associated with a respective location on the mooring line; on the data points obtaining a value associated with the location on the mooring line; determining parameters of an equation describing an anchor line curve from the values associated with the location on the mooring line for the data points; calculating at a predetermined position on the mooring line a line angle of the mooring line from a derivative of the equation at the predetermined position based on the parameters.

A ship information displaying system can easily grasp the size of a ship intuitively. The system includes a position detector that detects a position of a ship and generates positional data, a direction detector that detects a heading of the ship and generates directional data, a memory that stores size data indicative of a flat dimension of the ship on the basis of a detected location of the positional data in the ship, processing circuitry that places a ship object indicative of the ship at a position in a virtual three-dimensional space corresponding to the positional data, with an orientation corresponding to the directional data and a size corresponding to the size data and that generates an image containing the ship object by rendering a scene based on a viewpoint position and a line-of-sight direction set in the virtual three-dimensional space, and a display that displays the image.

A ship information displaying system can easily grasp the size of a ship intuitively. The system includes a position detector that detects a position of a ship and generates positional data, a direction detector that detects a heading of the ship and generates directional data, a memory that stores size data indicative of a flat dimension of the ship on the basis of a detected location of the positional data in the ship, processing circuitry that places a ship object indicative of the ship at a position in a virtual three-dimensional space corresponding to the positional data, with an orientation corresponding to the directional data and a size corresponding to the size data and that generates an image containing the ship object by rendering a scene based on a viewpoint position and a line-of-sight direction set in the virtual three-dimensional space, and a display that displays the image.

An estimation method executed by a computer, the estimation method includes estimating a fouling degree of a target ship based on an accumulation period since a most recent maintenance on the target ship; selecting, as training data, one piece of data that has a fouling degree that resembles the estimated fouling degree, from among a plurality of pieces of data, when generating a machine learning model for estimating an amount of fuel consumption due to navigation; and generating the machine learning model based on the selected training data.

An estimation method executed by a computer, the estimation method includes estimating a fouling degree of a target ship based on an accumulation period since a most recent maintenance on the target ship; selecting, as training data, one piece of data that has a fouling degree that resembles the estimated fouling degree, from among a plurality of pieces of data, when generating a machine learning model for estimating an amount of fuel consumption due to navigation; and generating the machine learning model based on the selected training data.

A computer implemented method and apparatus for a marine vessel data system, the method comprising: receiving data from at least one sensor configured to measure vibration and operationally arranged to the marine vessel to provide time-domain reference sensor data; maintaining the time-domain reference sensor data within a data storage system; generating a Fast Fourier Transform (FFT) on the time-domain reference sensor data to provide a plurality of reference spectra files in frequency-domain, wherein each reference spectra file comprises spectra data defined by amplitude information and frequency information, and each spectra file is associated with condition information determined based on collection of the time-domain reference sensor data; normalizing each reference spectra file by converting the frequency information to order information using the condition information to provide normalized reference spectra files; and training a convolutional autoencoder type of neural network using the normalized reference spectra files.

Method performed by a first communication device (101) operating in a wireless communications network (100). The first communication device (101) obtains (201) a first set of one or more values indicating an observed speed of a boat (151) relative to a power of an engine (161) of the boat (151). The speed and the first indication are obtained by from sensors (171, 172) in the boat (151). The first communication device (101) then obtains (205) a second indication of an external factor on the hull of the boat (151) causing friction against water. The obtaining (205) of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device (101) also initiates providing (206) a third indication of the external factor to a device (103), based on the obtained second indication.

Method performed by a first communication device (101) operating in a wireless communications network (100). The first communication device (101) obtains (201) a first set of one or more values indicating an observed speed of a boat (151) relative to a power of an engine (161) of the boat (151). The speed and the first indication are obtained by from sensors (171, 172) in the boat (151). The first communication device (101) then obtains (205) a second indication of an external factor on the hull of the boat (151) causing friction against water. The obtaining (205) of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device (101) also initiates providing (206) a third indication of the external factor to a device (103), based on the obtained second indication.

A marine vessel advisory system may be configured to calculate and provide operational information that show fuel consumption savings based on adjustment of vessel speed and/or heading. In an embodiment, the advisory system may operate real-time to collect operational and/or environmental conditions information to be used to calculate alternative operational performance of the marine vessel that will save fuel and reduce emissions. The calculations may include a simulation, machine learning, and/or artificial intelligence to determine a speed and/or heading of the marine vessel that will reduce fuel consumption. The advisory system may display the computed information for the operator, and the operator may elect to switch to the alternative operating parameters (e.g., slower speed). In an embodiment, the advisory system may interact directly with a marine vessel system and automatically cause the marine vessel system to adjust operating parameters based on computed operating parameters that saves fuel and reduces emissions.