Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B17/00—Systems involving the use of models or simulators of said systems
  • G05B17/02—Systems involving the use of models or simulators of said systems electric

Definitions

• the invention relates to marine vessels, their properties and in particular to fuel efficiency and operational performance relating to the properties of a marine vessel.
• the vessel characteristic parameters are determined to some extent. These characteristic parameters comprise, for example, block coefficient, breadth-draft-ratio, admiralty coefficient, and similar.
• Characteristic parameters of an itinerary comprise, for example, average water temperature, average air temperature, average speed, the length of the itiner ⁇ ary in time or nautical miles and average wind.
• the expression itinerary' may be a complete scheduled voyage or scheduled sections of a voyage. For example, when a vessel makes a return voyage visiting one destination there are several dif ⁇ ferent phases comprising departing from the first port, sailing at sea, arriving at the destination, port time at the destination, departing from the destination, sailing at sea and arriving at the first port.
• the expression x phase' is used for the above mentioned and similar phases, wherein the itinerary is a voyage having a schedule and route and may include several different phases.
• the operational characteristic parameters are characteristic parame ⁇ ters describing how the vessel was operated during the itinerary.
• the interesting operational characteristic parameters are not necessarily the same in the port and sailing at sea phases.
• a drawback of the prior art is that the oper- ational benchmarking is based on one marine vessel and is not comparable to others.
• a further drawback of the prior art is that historical data related to a new, previously unknown marine vessel is not available, and thus it is impossible to make measurements based on the historical data of the marine vessel.
• a further drawback of prior art is that the historical data re ⁇ ceived from other marine vessels is not necessarily applicable - even if the other marine vessel is a sis ⁇ ter vessel of the marine vessel being benchmarked - due to small differences in vessels and itineraries.
• a further drawback of the prior art is that according to the prior art method it is very hard to determine if a marine vessel is suitable for a desired itinerary and what level of operational performance may be expected.
• the invention discloses a method for deter ⁇ mining statistical distribution of characteristic pa ⁇ rameters for a marine vessel.
• information a plurality of charac ⁇ teristic parameters may be measured. For example, it is possible to measure trim, speed, wind, and similar. From the measurements it is possible to compute devia ⁇ tions from predetermined references and impact of de ⁇ viations on performance. As a result it is possible to get information, such as how much fuel efficiency may be improved if the marine vessel was trimmed optimal ⁇ ly. Correspondingly the optimal speed leads to im ⁇ proved fuel efficiency. Further examples of results include rudder and stabilizer usages. From this infor- mation it is possible to compute characteristic param ⁇ eters.
• the characteristic parameters received as re ⁇ sults may be caused by the crew or, for example, auto ⁇ pilot adjustments.
• the common nominator is that by correct interpretation they may be used in improving performance, for example, the fuel efficiency of a ma ⁇ rine vessel.
• the present invention discloses a method for determining characteristic parameters of a marine ves ⁇ sel.
• data from a marine vessel is first received.
• the received data is accumulated to a database. From the database itineraries are selected and then characteristic parameters for each itinerary are computed.
• a statistical model is created from said selected itineraries based on said characteristic pa ⁇ rameters. From the statistical model it is possible to estimate statistical distribution of at least one characteristic parameter.
• the data is classified based on vessel and itinerary characteristics .
• itineraries are split into phases comprising in port, departure, at sea and arrival.
• the method further comprises computing normalized characteristic parame ⁇ ters for the statistical model.
• the method further comprises comparing actual measured itinerary to the estimated statistical distribution.
• the step of selecting characteristic parameters further comprises selecting operational characteristic parameters.
• the method further com ⁇ prises computing characteristic parameters from said received data for each itinerary.
• the present invention maybe implemented as a computer program that is configured to perform the method described above when executed in a computing device .
• An embodiment of the present invention com- prises a system for determining characteristic parame ⁇ ters of a marine vessel, the system comprising data communication means for receiving data from a marine vessel, software execution means for executing computer programs and a database.
• the system is config- ured to perform the method described above preferably by executing the computer program described above.
• the expression x similar' means marine vessels or itineraries that are most similar in the statistical model. For example, if a new type of marine vessel is considered the most similar marine vessels and itineraries in the statis ⁇ tical model are interpreted to be similar ones. The actual differences between the features of the vessels vary significantly - that is, it is possible that a series of marine vessels have been manufactured with little or no changes at all. In this case the most similar vessels are almost exactly the same. On the contrary, it is possible that a new type of marine vessel is manufactured so that also the most similar ones are quite different, however, they are the most similar in the statistical model and thus they are chosen by the model. Similar considerations may be ap ⁇ plied for itineraries.
• a benefit of the present invention is that by using the method according to the present invention it is possible to determine a base line for key perfor ⁇ mance indicators for previously unknown marine vessels or itineraries.
• a further benefit of the present invention is that it is possible to determine from collected and analyzed data the operations that have most potential for further improvements.
• the present invention provides means for comparing the operational performance of a marine vessel to other similar vessels and itinerar- ies. Improved own history is not always a sign of good operational performance and the present invention pro ⁇ vides information about the improvement potential and how it can be achieved.
• the present invention provides improved perfor ⁇ mance, for example fuel efficiency, of a marine ves ⁇ sel .
• a further benefit of the present invention is that the method allows also other types of classifica- tion of marine vessels and itineraries. For example, it is possible to plan itineraries or marine vessels for desired itineraries or regions by estimating oper ⁇ ational characteristic parameters that are typical for certain regions. For example, some operational charac- teristic parameters may be better for vessels operated in the Baltic Sea, rather than the Caribbean Sea, or vice versa.
• a benefit of the present inven ⁇ tion is the possibility to assess if the crew has op- erated the marine vessel efficiently compared to other similar marine vessels or other similar itineraries performed with the same marine vessel by different crews or the same crew at different times.
• Fig. 1 is a block diagram of an example embodiment of the present invention.
• Fig. 2 is a flow chart of a method according to the present invention.
• FIG 1 a block diagram of a system according to the present invention is disclosed.
• the system comprises computing means 11 coupled with a da ⁇ tabase 12.
• computing means 11 is a server and the database 12 may be integrated to the server, however, external databases are preferred when the da ⁇ tabase size is very large.
• the server of the embodi ⁇ ment of figure 1 comprises data communication means for receiving data from marine vessels 13.
• marine vessels 13 have wireless data communication means, however, the server does not need to have wire ⁇ less data communication means but can use available networks for communicating with marine vessels 13.
• Vessels 13 may send the data in "real time" or as a batch after the itinerary. It is also possible to use other means for data communication, such as memory card, portable disk drive or similar media for trans ⁇ ferring the data from a vessel to the server.
• Figure 2 discloses a flow chart of an example embodiment of the present invention.
• the first step is to receive data from a marine ves ⁇ sel, step 20. This data is then accumulated to a data ⁇ base, step 21.
• the data is received independently from a plurality of marine ves ⁇ sels, however, the invention can be limited to one ma- rine vessel.
• the data received from a marine vessel is accumulated to the database in order to generate a large database containing information related to characteristic parameters for a plurality of different vessels in different itineraries.
• the characteristic parameters the information relates to include vessel characteristic parameters, itinerary characteristic parameters and operational characteristic parameters.
• the received data may be split into phases in the database storage, step 22.
• the data received from a marine vessel may relate to phases, wherein the marine vessel is in the port, departing from the port, sailing at sea and arriving at the port.
• the step of splitting may be performed before or after the database storage.
• the purpose of the step of splitting is to facilitate selecting itineraries from the database for statistical model creation, step 23.
• Phases selected to each itinerary depend on the characteristic parame ⁇ ters to be analyzed. For example, it is possible to select phases from data that relate to the time at the port if a characteristic parameter relating to the port time, such as the efficiency of air conditioning compressors, is analyzed.
• step 24 the method comprises computing characteristic parameters for the statistical model to be created. Characteristic parameters may be computed to the database, however, they are necessary only at the statistical modeling step.
• the statistical model used may be chosen from a plurality of different mod ⁇ els, for example, Self Organizing Map or Neural Gas. Operational characteristic parameters need not be stored in the statistical model.
• characteristic parameters are in a normalized form.
• the significance of the normalization is to provide comparable data from marine vessels of different types and sizes when the absolute values measured from different types of marine vessels are not comparable.
• the normalization process as such is known to a person skilled in the naval architecture as the normalization process is used when designing and manufacturing a marine vessel. Typically, first a min- iature model is manufactured and measured. Then the measurement results are normalized so that interpreta ⁇ tions can be made for a marine vessel in real scale.
• the characteristic parameters are normalized in order to provide comparability be- tween marine vessels. Examples of normalized charac ⁇ teristic parameters include, for example, computing resistance coefficient or other coefficients.
• normalization means, for example, that instead of variance of absolute speed the relative change is used.
• itinerary characteris ⁇ tic parameters are normalized in order to provide com ⁇ parability between different itineraries. For example, instead of average speed Froude Number Fn may be com ⁇ puted. Furthermore, instead of using the variance in the speed it is typical to measure what portion of to ⁇ tal propulsion energy the variance has caused in terms of fuel efficiency.
• the statistical model for further analy ⁇ sis is created, step 25.
• the statistical model is a self organizing map or sim- ilar.
• the self organizing map comprises data points or samples that each include information about vessel, itinerary and operational characteristic parameters.
• the self organizing map classifies data according to vessel and itinerary characteristic parameters.
• a refer ⁇ ence data point is selected, step 26.
• the reference data point may be a new data point or already involved in the creation of the statistical model.
• the reference data point it is possible to pick from the self organizing map, for example, ten most similar data points and request the distance be ⁇ tween the reference and similar data points.
• the statistical distri- butions of characteristic parameters of interest are estimated by weighting the data points based on the distances between the reference and the data point in question.
• the actual operational performance related to a particular itinerary may be compared to the esti- mated statistical distribution. Then it is possible to say if the marine vessel compares well to other ves ⁇ sels.
• Bayesian regression may be used for providing the statistical distribution.
• a self organizing map was used.
• Other statistical meth ⁇ ods, such as Bayesian regression may require small modifications to the method disclosed in figure 2.
• the present in ⁇ vention is implemented as computer software that is installed in the server of Figure 1 or other similar computing device.
• the comput- er program mentioned above is embodied in a computer readable medium.
• the computer program embodied in the computer readable medium is executed in the server.
• the server incorporates conventional computing means that are capable of executing computer programs. It is ob ⁇ vious to a person skilled in the art that with the ad- vancement of technology, the basic idea of the inven ⁇ tion may be implemented in various ways. The invention and its embodiments are thus not limited to the exam ⁇ ples described above; instead they may vary within the scope of the claims.

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Citations (7)

• 2010
  • 2010-12-31 FI FI20106391A patent/FI20106391A0/fi not_active Application Discontinuation
• 2011
  • 2011-12-30 WO PCT/FI2011/051170 patent/WO2012089926A1/en not_active Ceased
  • 2011-12-30 FI FI20135754A patent/FI20135754L/fi not_active IP Right Cessation

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