EP3854747A1 - Dispositif, système et procédé pour filtrer un signal de position dans la compensation active du tangage - Google Patents

Dispositif, système et procédé pour filtrer un signal de position dans la compensation active du tangage Download PDF

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Publication number
EP3854747A1
EP3854747A1 EP20153116.7A EP20153116A EP3854747A1 EP 3854747 A1 EP3854747 A1 EP 3854747A1 EP 20153116 A EP20153116 A EP 20153116A EP 3854747 A1 EP3854747 A1 EP 3854747A1
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EP
European Patent Office
Prior art keywords
acceleration data
instant
data point
previous
output
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP20153116.7A
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German (de)
English (en)
Inventor
Wojciech Pomierski
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nat Oilwell Varco Poland Sp Z O O
National Oilwell Varco Poland Sp zoo
Original Assignee
Nat Oilwell Varco Poland Sp Z O O
National Oilwell Varco Poland Sp zoo
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Application filed by Nat Oilwell Varco Poland Sp Z O O, National Oilwell Varco Poland Sp zoo filed Critical Nat Oilwell Varco Poland Sp Z O O
Priority to EP20153116.7A priority Critical patent/EP3854747A1/fr
Publication of EP3854747A1 publication Critical patent/EP3854747A1/fr
Withdrawn legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B27/00—Arrangement of ship-based loading or unloading equipment for cargo or passengers
    • B63B27/10—Arrangement of ship-based loading or unloading equipment for cargo or passengers of cranes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/02—Devices for facilitating retrieval of floating objects, e.g. for recovering crafts from water
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66D—CAPSTANS; WINCHES; TACKLES, e.g. PULLEY BLOCKS; HOISTS
    • B66D1/00—Rope, cable, or chain winding mechanisms; Capstans
    • B66D1/28—Other constructional details
    • B66D1/40—Control devices
    • B66D1/48—Control devices automatic
    • B66D1/52—Control devices automatic for varying rope or cable tension, e.g. when recovering craft from water
    • B66D1/525—Control devices automatic for varying rope or cable tension, e.g. when recovering craft from water electrical
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B17/00—Vessels parts, details, or accessories, not otherwise provided for
    • B63B2017/0072—Seaway compensators

Definitions

  • the present invention relates to a device, system and method for position signal filtering in active heave compensation. More particularly, the invention relates to a method, device and system where input data to a Kalman filter is pre-filtered and forecasted to improve positioning in active heave compensation. The invention also relates to a non-transitory computer readable medium encoded with instructions that, when executed by a control unit, cause the control unit to execute the method.
  • Wave-generated motion of floating vessels is usually measured by a so-called motion reference unit (MRU).
  • MRUs measure vessel motion from typically 6 sensors: 3 accelerometers and 3 gyros.
  • the accelerometers pick up accelerations along 3 perpendicular axes while the gyros measure the angular acceleration around the same axes.
  • the sensors cover a range of frequencies spanning typically from 0 to 100 Hz.
  • a MRU is able to estimate linear and angular accelerations, velocities and positions in any direction and at any predetermined measurement point of the vessel.
  • the details on how sensor signals are combined to determine the heave acceleration at the vessel center of gravity, are known to the person skilled in the art but are outside the scope of this description.
  • a Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe.
  • velocity equals the time integral of acceleration and position equals the time integral of velocity.
  • the accelerometers are not perfect but possess some small but finite noise, it is not possible to derive true velocity or positions from the accelerometer signals by pure integration. Even a tiny offset error or false low frequency component will grow to very high values during some extended integration time interval with some type of filter.
  • a standard Kalman or extended Kalman filter will provide satisfactory results under certain conditions, but often this is not sufficient.
  • No physical wave model is available for Kalman filters, so in case of input acceleration data noise or even void data points, predictions need to be made based on previous input acceleration data points, which causes signal delays.
  • the invention has for its object to remedy or to reduce at least one of the drawbacks of the prior art, or at least provide a useful alternative to prior art.
  • the invention relates to an apparatus for filtering and forecasting of acceleration data from an MRU as set forth in claim 1.
  • the invention in a second aspect, relates to a system for controlling the position of a load on a rig or vessel as set forth in claim 3.
  • the invention relates to a rig or vessel including the system according to the second aspect.
  • the invention relates to a method for filtering and forecasting of acceleration data from an MRU as set forth in claim 7.
  • the invention relates to a non-transitory computer readable medium encoded with instructions that, when executed by a control unit, cause the control unit to execute the method according to the fourth aspect.
  • the reference numeral 1 will be used to denote an apparatus according the first aspect of the invention, whereas the reference numeral 10 will be used to denote a system according to the second aspect of the invention.
  • the drawings, graphs and flow diagrams are shown schematically and simplified, and various features in the drawings are not necessarily drawn to scale.
  • Figure 1 shows, very schematically, a system 10 for controlling the position of a load on a rig or vessel.
  • the system includes an apparatus 1, denoted “Filter & Integrator” in the figure, for filtering a forecasting of acceleration data from a motion reference unit (MRU) 2.
  • MRU motion reference unit
  • the MRU 2 samples the acceleration, from the influence of waves and wind, of a not shown rig or vessel.
  • the sampling frequency (1/ ⁇ t, where ⁇ t is used in Figs. 3-5 to denote the time between two samples) may vary in different situations, but in exemplary embodiments the sampling frequency may be in the order to 100Hz or higher. In certain embodiment the sampling frequency may be in the order or 500 Hz or higher.
  • the apparatus 1 receives the acceleration data from the MRU 2 and integrates, pre-filters and forecasts it before it is transmitted to a controller 4 provided with a Kalman filter for further processing of velocity data and ultimately for calculating the position of the rig or vessel.
  • the apparatus is also adapted to build a string of values based on deviation between instant and previous acceleration data points for forecasting of acceleration data in case of noise or void data points as will be explained below with reference to Figs. 3-5 .
  • the controller 4 is adapted to calculate a change in position of the rig or vessel, and to operate a heave compensator 6, see Fig.
  • FIG. 2 An exemplary hydraulic heave compensator 6 is shown in Fig. 2 , wherein hydraulic pistons 12 are operated to counteract the movement/heave of the rig or vessel due to waves and wind.
  • An exemplary, schematically shown load-handling device 14, is here represented by two sheaves over which a wire rope is running.
  • the controller 4 is adapted to control the hydraulic pistons 12 to lower and raise the platform 16 to which the sheaves 14 are connected in response to the calculated change of position.
  • Fig. 3 shows a flow diagram exemplifying the steps taken by the apparatus according to the present invention as well as in the method according to the present invention to provide an instant output acceleration data point.
  • a series of acceleration data points are sampled by the MRU 2 and transmitted to the apparatus 1.
  • the latest/current acceleration data point will herein be referred to as an instant acceleration data point.
  • Acceleration data points sampled at previous points in time, i.e. before the instant acceleration data point will be referred to as previous acceleration data points.
  • step 3A an instant, measured acceleration data point a n for point n in time is sampled and transmitted to the apparatus 1 for any n larger than n k , where n k is a number of samples needed to start the calculations.
  • n k will vary in different embodiments and will also be dependent on the sampling time, but in certain embodiments n k may be in the order of 5 or 10.
  • step 3B an instant change in acceleration at n between the instant measured acceleration data point a n and the immediate previous output acceleration data point af n-1 over the sampling period ⁇ t is calculated.
  • the derivate of acceleration with respect to time is sometimes also referred to as "jerk", whereby at n may be regarded as the instant jerk.
  • An average change in acceleration as n over n k samples is calculated in step 3C.
  • step 3D the difference between the average change in acceleration and the immediate, previous average change in acceleration k n is calculated.
  • step 3E The average k n over n s samples, denoted ks n, , is then then eventually calculated in step 3E.
  • ks n can be regarded as an arithmetic string of values that are used for forecasting acceleration values in case of void or noisy acceleration data points as will now be explained.
  • step 3F the absolute value of at n , i.e the instant change in acceleration over ⁇ t as calculated in step 3B, is checked towards a predetermined maximum value at max .
  • This predetermined value may be set differently in different embodiments, for different MRUs and in different weather conditions, but for any absolute value of at n larger than at max the instant, measured acceleration data point a n in step 3A will assumed to be noise and therefore disregarded.
  • a n from step 3A will also be disregarded. If a n is disregarded, then the process continues to the right in the flow diagram of Fig. 3 , where the immediate previous calculated value ks n-1 in the arithmetic string from step 3E is used to forecast an instant output, acceleration data point.
  • the previous calculated value ks n-1 in the arithmetic string from step 3E is set as the instant value ks n in the arithmetic string.
  • step 3X an updated average change in acceleration as n is calculated based on ks n from step 3W and the immediate previous average change in acceleration as n-1 .
  • step 3Y the instant, output acceleration data point af n is calculated based on the immediate, previous output acceleration data point af n-1 , the updated average change in acceleration as n from step 3X and the sampling time ⁇ t.
  • step 3Z the calculated as n from step 3X is set at at n for use as future at n-1 in step 3C.
  • the forecasted acceleration data point af n from step 3Y is thereby taken as output acceleration data point from the apparatus.
  • step 3F if the absolute value of at n does not exceed at max and if neither of a n or a n-1 is void, then the instant, measured/sampled acceleration data point a n from step 3A is accepted as the instant output acceleration data point a f n as shown in 3G.
  • the acceleration forecast model as calculated in steps 3A-E, is also then also accepted, and ks n is accepted as an updated data point in the arithmetic string and may be used in a future ks n-1 in step 3W in a situation where at n is disregarded.
  • step 3H the measured or forecasted af n is taken as output from the acceleration flow diagram.
  • the pre-filtration and forecasting of acceleration data points as exemplified in Fig. 3 will significantly improve the quality of input velocity data points to a Kalman filter in the controller 4, as will be explained with reference to the following figures.
  • Fig. 4 an exemplary flow diagram shows how the acceleration output data points from Fig. 3 are used to calculate both measured and modelled velocity data points to the Kalman Filter in the controller 4.
  • output acceleration data points af n are taken from the algorithm in Fig. 3 for n larger than n c , where n c is a number over which the change in output velocity will later be averaged.
  • n c may vary between different embodiments and in different weather conditions, and it will also depend on the time between samples ⁇ t, but in certain embodiments n c may be in the range of 5 or 10.
  • step 4B an instant velocity data point vm n , without offset, is found from the immediate, previous velocity data point and the integration of input acceleration from step 4A over ⁇ t.
  • step 4C instant position data point s1 n , without offset, is found from the immediate, previous position data point s1 n-1 and integration of the velocity from step 4B.
  • step 4D it is checked if n is smaller than or equal to n i , where n i is a number/gate set to ensure a sufficient number of samples after start-up when calculating the velocity offset v0 n .
  • n i may in certain embodiments be a number that is large enough for the passing of one or more heave periods, which are typically around 12 seconds each.
  • n i may be set to ensure the inclusion of around 5 heave periods, whereby n i may be in the order of 6000 (1 minute of 100 Hz sampling frequency) or 30 000 (1 minute of 500 Hz sampling frequency).
  • the method goes to the right in the flow diagram to step 4X, where the offset is estimated as sum of position data points from start-up at t 0 to the present time t n over the time lapsed. If a sufficient number of data points have been sampled, i.e. if n > n i , then in step 4E the same calculation is done based on position data points from time t n-ni .
  • step 4F An actual measured instant, output velocity data point v n , including offset v0 n , is then calculated in step 4F, and used as measurement input to the Kalman filter in step 4M.
  • a velocity model is built as a further input to the Kalman filter based on previous output velocity data points from the Kalman filter, as will be now be explained.
  • step 4G an instant change in output velocity vt n is calculated based on the immediate two previous output velocity data points from the Kalman filter vf n-1 and vf n-2 .
  • An average change in output velocity vs n over n c samples is calculated in step 4H, where n c in exemplary embodiments may be in the order of 5 or 10, depending i.a.
  • step 41 The difference in change in average output velocity dvs n between the present value vs n and the previous value vs n-1 is calculated in step 41.
  • step 4J it is checked if the absolute value of dvs n exceeds a pre-determined value dvs max . If the absolute value of dvs n exceeds dvs max , then the instant calculated value of dvs n will be disregarded, and the previous value dvs n-1 will be used instead as indicated in step 4K. Normally, since the acceleration data has already been filtered (as shown in Fig. 3 ), the absolute value of dvs n will not be exceeding dvs max .
  • the new (or optionally previous) dvs n from step 4J (or optionally 4K), will then be used to calculate an instant modelled velocity data point ve n in step 4L as input to the Kalman filter in step 4M.
  • the immediate previous output velocity data point from the Kalman filter vf n-1 is used together with vs n from step 4H and dvs n from step 4J (or optionally 4K) and ⁇ t.
  • the instant modelled velocity data point ve n is then used as input to the Kalman filter together with the measured velocity v n in step 4M. What happens "behind the curtains" of the Kalman filter is beyond the scope of the present disclosure, but will be known to a person skilled in the art.
  • the Kalman filter produces an instant output velocity data point vf n in step 4N.
  • Fig. 5 the instant output velocity data point vf n from Fig. 4 is used by the controller in step 5A.
  • vf n is then used, together an immediate previous position data point sm n-1 to find the immediate, instant position data point without offset sm n in step 5B.
  • an instant offset variable st n is calculated based on an immediate previous offset variable st n-1 and sm n from step 5B.
  • step 5D a check is made to verify if n is smaller than n d where n d is a number/gate set to ensure a sufficient number of samples after start-up when calculating the position offset.
  • n d will typically be in the range 5 times n i in step 4E.
  • step 5X the position offset is estimated as the sum of position data points st n from start-up at t 0 to the present time t n over the time lapsed. If a sufficient number of data points have been sampled, i.e. if n>n d , then in step 5E the same calculation is done based on position data points from time t n-nd . A resulting instant calculated output sf n from the controller is finally found in step 5F based on the integrated position from step 5B and the calculated offset.
  • the output sf n represents the measure and calculated instant, output position of the rig or vessel on which the system 10 and apparatus 1 are placed, and is used by the controller to counteract the change in position by operating the heave compensator.
  • Fig. 6 shows MRU-sampled values a n (some of which have been manually spoiled to test the algorithm) together with the output acceleration data values after filtering and forecasting.
  • a n many of which have been manually spoiled to test the algorithm
  • Fig. 7 the measured velocity v n is shown together with the forecasted velocity ve n and the output from the Kalman filter vf n .
  • Velocity data output from the Kalman filter without the pre-filtering and forecasting of acceleration data has also been shown, and as can be seen from the figure, the pre-filtering and forecasting algorithm according to the present invention, significantly improves the accuracy of the results.
  • Fig. 8 is an enlarged view of a portion of the graph from Fig. 7 . As can be seen from Figs. 7 and 8 , there is a very good match between measurement and model inputs to the Kalman filter and the output from the filter when the acceleration data has been pre-filtered and forecasted.
  • Fig. 9 shows a comparison between the input, sampled acceleration data a n , acceleration data with filtration and forecasting af n compared to real acceleration data as found by means of Jonswap wave model, as will be understood by a person skilled in the art and not disussed in further detail herein. As can be seen, there is a remarkably good fit between the forecasted acceleration data and the real model, despite noise in the input samples.
  • Fig. 10 shows load position error in active heave compensation based on input to the heave compensator from the system 10 according to the present invention. As can be seen from the figure, it is possible to keep a 100mT load fixed within an error margin of approximately ⁇ 10 cm.
  • aspects of invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.
  • the device claim enumerating several means several of these means may be embodied by one and the same item of hardware.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Ocean & Marine Engineering (AREA)
  • Feedback Control In General (AREA)
EP20153116.7A 2020-01-22 2020-01-22 Dispositif, système et procédé pour filtrer un signal de position dans la compensation active du tangage Withdrawn EP3854747A1 (fr)

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EP20153116.7A EP3854747A1 (fr) 2020-01-22 2020-01-22 Dispositif, système et procédé pour filtrer un signal de position dans la compensation active du tangage

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EP20153116.7A EP3854747A1 (fr) 2020-01-22 2020-01-22 Dispositif, système et procédé pour filtrer un signal de position dans la compensation active du tangage

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2123588A1 (fr) * 2008-05-21 2009-11-25 Liebherr-Werk Nenzing GmbH Commande de grue dotée d'une suite active de houle
WO2011034435A1 (fr) * 2009-09-16 2011-03-24 Kongsberg Seatex As Procédé et système permettant la modélisation d'accélérations rotatives d'un bateau
WO2012161584A1 (fr) * 2011-05-20 2012-11-29 Optilift As Système, dispositif et procédé pour suivre position et orientation de véhicule, dispositif de chargement et chargement dans opérations de dispositif de chargement

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2123588A1 (fr) * 2008-05-21 2009-11-25 Liebherr-Werk Nenzing GmbH Commande de grue dotée d'une suite active de houle
WO2011034435A1 (fr) * 2009-09-16 2011-03-24 Kongsberg Seatex As Procédé et système permettant la modélisation d'accélérations rotatives d'un bateau
WO2012161584A1 (fr) * 2011-05-20 2012-11-29 Optilift As Système, dispositif et procédé pour suivre position et orientation de véhicule, dispositif de chargement et chargement dans opérations de dispositif de chargement

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