METHOD FOR TRAINING AT LEAST ONE ARTIFICIAL INTELLIGENCE MODEL FOR ESTIMATING THE WEIGHT OF AN AIRCRAFT DURING FLIGHT BASED ON USE DATA

Abstract

A method for training at least one artificial intelligence model for estimating the weight of an aircraft during flight based on use data, the at least one artificial intelligence model being developed in order to be implemented during at least one predetermined flight phase of at least one aircraft of the same type. The method comprises carrying out a plurality of flights and, for at least one of the plurality of flights, the method comprises acquiring, during flight, at least one set of flight data, carrying out at least one consistency test in order to check that a reliable reference weight is calculated or capable of being calculated, calculating at least one calculated weight of the aircraft and storing the at least one set of flight data and the at least one calculated weight.

Claims

1. A method for training at least one machine learning artificial intelligence model, the at least one machine learning artificial intelligence model being configured to be stored in a memory equipping an aircraft or a ground station, and developed in order to be implemented during at least one predetermined flight phase of at least one aircraft of the same type, the method comprising carrying out a plurality of flights, for at least one of the plurality of flights, the method comprises acquiring, during flight, at least one set of flight data acquired with several sensing devices at the same point in time t, wherein, for the plurality of flights, the method comprises the following steps: carrying out at least one predetermined consistency test with at least one consistency controller, the at least one consistency test enabling to check that a reliable reference weight is calculated or capable of being calculated; calculating, with at least one weight calculation controller, at least one calculated weight of the aircraft as a function of at least one weight chosen from the group comprising a current boarded fuel weight, a consumed fuel weight, a previously measured weight of the aircraft, an empty weight of the aircraft, a payload previously input by a user, a weight measured by a piece of connected equipment, and the reference weight; and if the at least one consistency test is validated, storing the at least one set of flight data and the weight(s) calculated at the point in time t, each stored set of flight data associated with a calculated weight forming a set of training data for the at least one machine learning artificial intelligence model, and wherein the method comprises using the sets of training data to program the at least one machine learning artificial intelligence model, the at least one machine learning artificial intelligence model being configured to estimate, at any time, an estimated instantaneous weight of the aircraft, or another aircraft of the same type, based on a current set of flight data.

2. The method according to claim 1, wherein the reference weight is defined as a function of an estimated weight of the aircraft based on measurements from several sensors, each sensor being arranged on each landing gear of the aircraft, the at least one consistency test comprising calculating a difference between the calculated weight and the estimated weight and then checking that the difference is less than a difference threshold value.

3. The method according to claim 1, wherein the reference weight is defined as a function of a checked theoretical weight of the aircraft, the at least one consistency test comprising checking the parameterization of the checked theoretical weight.

4. The method according to claim 1, wherein the reference weight is defined as a function of a calculated theoretical weight of the aircraft, the calculated theoretical weight being obtained by the at least one weight calculation controller based on at least one piece of information parameterized with a human-machine interface of the aircraft by the user prior to the at least one consistency test, the at least one consistency test comprising checking hat the at least one piece of information has been parameterized.

5. The method according to claim 1, wherein the flight data has data relating to at least two flight parameters chosen from the group comprising a speed of the aircraft in relation to the air, a vertical speed of the aircraft in relation to the ground, a longitudinal speed of the aircraft in relation to the ground, a lateral speed of the aircraft in relation to the ground, a vertical acceleration of the aircraft in relation to the ground, a flow of fuel supplying an engine of the aircraft, a rotational speed of a rotor equipping the aircraft, a wind direction, a wind speed, a quantity of fuel on board the aircraft, a yaw trajectory of the aircraft, the attitude of the aircraft, an air density, an air temperature, an altitude of the aircraft an angle of attack of a wing of the aircraft, a power consumed by at least one engine of the aircraft, positions of flight controls and positions of blades of a rotor and/or of a propeller.

6. The method according to claim 1, wherein, prior to the use of the sets of training data, the method comprises a count for counting the number N of the sets of training data and a comparison between the number N and a predetermined threshold value S.

7. The method according to claim 6, wherein the use of the sets of training data is implemented when the number N is greater than or equal to the predetermined threshold value S.

8. The method according to claim 1, wherein the at least one machine learning artificial intelligence model comprises a first model and a second model different from the first model, the first model being associated with a first predetermined flight phase from the at least one predetermined flight phase and the second model being associated with a second predetermined flight phase from the at least one predetermined flight phase, the first predetermined flight phase being different from the second predetermined flight phase.

9. The method according to claim 1, wherein the at least one predetermined flight phase is chosen as a function of a required accuracy of the at least one machine learning artificial intelligence model for estimating, at any time, the estimated instantaneous weight of the aircraft, or an aircraft of the same type, based on a current set of flight data.

10. The method according to claim 1, wherein the at least one predetermined flight phase is chosen as a function of a required dispersion of the at least one machine learning artificial intelligence model for estimating, at any time, the estimated instantaneous weight of the aircraft, or an aircraft of the same type, based on a current set of flight data.

11. The method according to claim 1, wherein the at least one predetermined flight phase is chosen as a function of a diversity of the plurality of flights performed by the user of the aircraft, or an aircraft of the same type.

12. The method according to claim 1, wherein the at least one machine learning artificial intelligence model is chosen from the group comprising decision tree algorithms, random forest algorithms, support vector machine algorithms, adaptive boosting algorithms, back-propagation algorithms, gradient boosting algorithms, neural network algorithms, and deep neural network algorithms.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0091] The disclosure and its advantages appear in greater detail in the context of the following description of embodiments given by way of illustration and with reference to the accompanying figures, wherein:

[0092] FIG. 1 is a side view diagram of an aircraft suitable for implementing a training method according to the disclosure;

[0093] FIG. 2 is a logic diagram showing steps of a training method according to the disclosure;

[0094] FIG. 3 is a diagram of a step of using training data for programming a machine learning artificial intelligence model, according to the disclosure; and

[0095] FIG. 4 is a logic diagram representative of the steps performed in order to carry out at least one predetermined consistency test according to the disclosure.

DETAILED DESCRIPTION

[0096] Elements that are present in more than one of the figures are given the same references in each of them.

[0097] As already mentioned, the disclosure relates to the field of estimating an estimated instantaneous weight of an aircraft at any point in time and, in particular, during the flight of an aircraft.

[0098] The disclosure more specifically relates to a method for training at least one machine learning artificial intelligence model configured to be stored in a memory equipping an aircraft or a ground station.

[0099] As shown in FIG. 1, an aircraft 1 such as a rotorcraft comprises a memory 2. Such a memory 2 is in particular designed to contain one or more machine learning artificial intelligence models developed to be implemented during at least one predetermined flight phase of one or several aircraft 1 of the same type.

[0100] Such machine learning artificial intelligence models will be described in greater detail in FIGS. 2 to 4.

[0101] Such an aircraft 1 therefore comprises several sensing devices 3, 3 of different natures for acquiring different data at the same point in time t and forming at least one set of flight data J1, J2.

[0102] A set of flight data therefore comprises several items of flight data different from each other and acquired at the same point in time t, these data each being representative of a flight parameter.

[0103] For example, the flight data provided by the sensing devices 3, 3 may have data relating to at least two flight parameters chosen from the group comprising a speed of the aircraft 1 in relation to the air, a vertical speed of the aircraft 1 in relation to the ground, a longitudinal speed of the aircraft 1 in relation to the ground, a lateral speed of the aircraft 1 in relation to the ground, a vertical acceleration of the aircraft 1 in relation to the ground, a flow of fuel supplying an engine 8, of the aircraft 1, a rotational speed of a rotor 9 equipping the aircraft 1, a wind direction, a wind speed, an angle of attack of the wing, a quantity of fuel on board the aircraft 1, a yaw trajectory of the aircraft 1, the attitude of the aircraft 1 defined by attitude angles around roll, pitch and yaw axes, an air density, an air temperature, an altitude of the aircraft 1, an angle of attack of a wing of the aircraft 1, a power consumed by at least one engine 8, 10 of the aircraft 1, positions of flight controls and positions of blades 11, 12 of a rotor 9, 19 and/or of a propeller 13.

[0104] Furthermore, the sensing devices 3, 3 may be connected to an avionics system already provided on the aircraft 1 comprising, but not limited to, a flight management system FMS, an air data computer ADC, an attitude and heading reference system AHRS, a radio altimeter Rad Alt and a satellite positioning system GNSS.

[0105] Moreover, each sensing device 3, 3 may comprise a physical sensor or several physical sensors and processing means.

[0106] These sensing devices 3, 3 may be connected by wired or wireless means to at least one weight calculation controller 5 equipping the aircraft 1 for processing and/or storing the sets of flight data J1, J2.

[0107] Alternatively, or additionally, the sensing devices 3, 3 may also be connected by wired or wireless means to at least one weight calculation controller 15 equipping a ground station 18 for processing and/or storing the sets of flight data J1, J2 outside the aircraft 1.

[0108] Moreover, the weight calculation controller or controllers 5, 15 may respectively comprise, for example, at least one processor and at least one memory, at least one integrated circuit, at least one programmable system, or at least one logic circuit, these examples not limiting the scope given to the expression controller. The term processor may refer equally to a central processing unit or CPU, a graphics processing unit or GPU, a digital signal processor or DSP, a microcontroller, etc.

[0109] Advantageously, such an aircraft 1 may comprise sensors 4 arranged on landing gears 6. These sensors 4 may also be connected by wired or wireless means to the weight calculation controller or controllers 5, 15 in order to estimate an estimated weight Me of the aircraft 1 and/or to at least one consistency controller 5, 15.

[0110] These sensors 4 may therefore be chosen from the group comprising force sensors, pressure sensors, displacement sensors, position sensors and deformation sensors.

[0111] Furthermore, the weight calculation controller or controllers 5, 15 and the consistency controller or controllers 5, 15 may be separate from each other or form a one-piece assembly, as shown in FIG. 1. However, the representation in FIG. 1 is only provided as a non-limiting example.

[0112] Moreover, such an aircraft 1 may comprise a human-machine interface 7 allowing the pilot of the aircraft 1, for example, to update a parameter linked to an item of information relating to a calculated theoretical weight Mtc of the aircraft 1. Furthermore, such a human-machine interface 7 is then connected by wired or wireless means to the consistency controller or controllers 5, 15.

[0113] As shown in FIG. 2, the method 20 comprises a plurality of flights being carried out 21 by a user of the aircraft 1 or by another user of another aircraft of the same type.

[0114] Therefore, for at least one of the plurality of flights, the method 20 comprises acquiring 22, during flight, at least one set of flight data J1, J2 acquired at the same point in time t with the sensing devices 3, 3 described above.

[0115] The method 20 then comprises carrying out 23 at least one predetermined consistency test with the controller or controllers 5, 15. For example, and as shown in FIG. 4, three consistency tests may be implemented separately in parallel. This or these consistency tests can therefore be used to check that a reliable reference weight Mref has already been calculated and is therefore stored or indeed that it is capable of being calculated.

[0116] Furthermore, the method 20 comprises calculating 24, with the controller or controllers 5, 15, at least one calculated weight Mc of the aircraft 1 as a function of at least one weight chosen from the group comprising a current boarded fuel weight, a consumed fuel weight, a previously measured weight of the aircraft 1, an empty weight of said aircraft 1, a payload previously input by a user, a weight measured by a piece of connected equipment such as a winch or a sling, and said reference weight Mref.

[0117] Then, if one of the consistency tests is validated, the method 20 comprises storing 25 the set or sets of flight data J1, J2 acquired at the point in time t of the flight and said at least one calculated weight Mc1, Mc2 at the same point in time t.

[0118] The weight calculation controller or controllers 5, 15 make it possible to associate each set of flight data J1, J2 with a corresponding calculated weight Mc1, Mc2 in order to form a set of training data {J1, Mc1}, {J2, Mc2} for the machine learning artificial intelligence model or models that may be contained in the onboard memory 2.

[0119] Therefore, such a set of training data {J1, Mc1}, {J2, Mc2} results from at least one flight carried out by the user of the aircraft 1 in real conditions during a mission and not during a test phase carried out by the constructor of the aircraft 1.

[0120] Before using the sets of training data {J1, Mc1}, {J2, Mc2} and according to one advantageous embodiment of the disclosure, the method 20 may also comprise a count 26 for counting the number N of sets of training data {J1, Mc1}, {J2, Mc2}.

[0121] In this case, the method 20 then comprises a comparison 27 between the number N and a predetermined threshold value S. Furthermore, the sets of training data {J1, Mc1}, {J2, Mc2} may be used 28 when this number N is greater than or equal to the predetermined threshold value S.

[0122] While the count 26 and comparison 27 steps are optional, the method 20 nevertheless comprises using 28 the sets of training data {J1, Mc1}, {J2, Mc2} to program the machine learning artificial intelligence model or models 50, 51, 52 as shown in FIG. 3.

[0123] Such machine learning artificial intelligence models 50, 51, 52 are further configured to estimate, at any time, using the sets of training data, an estimated instantaneous weight Mi of the aircraft 1 or another aircraft of the same type, based on a current set of flight data Jc.

[0124] Indeed, the sets of training data can be used to define calculation formulae or rules making it possible to calculate, for each predetermined flight phase, an estimated instantaneous weight Mi of the aircraft 1 directly as a function of a current set of flight data Jc.

[0125] Furthermore, said at least one machine learning artificial intelligence model 50, 51, 52 may comprise a first model 51 and a second model 52 separate from the first model 51. In this case, the first model 51 may be associated with a first predetermined flight phase of said at least one predetermined flight phase and the second model 52 may be associated with a second predetermined flight phase of said at least one predetermined flight phase. The first predetermined flight phase is separate from the second predetermined flight phase.

[0126] A particular machine learning artificial intelligence model 50, 51, 52 may therefore be associated with each predetermined flight phase. Each machine learning artificial intelligence model 50, 51, 52 may advantageously be chosen from the group comprising decision tree algorithms, random forest algorithms, support vector machine algorithms, adaptive boosting algorithms, back-propagation algorithms, gradient boosting algorithms, neural network algorithms, and deep neural network algorithms.

[0127] Moreover, the predetermined flight phase or phases may be chosen as a function of a required accuracy of the machine learning artificial intelligence model or models 50, 51, 52 for estimating, at any time, said estimated instantaneous weight Mi of said aircraft 1, or an aircraft of the same type, based on a current set of flight data Jc.

[0128] The predetermined flight phase or phases may also be chosen as a function of a required dispersion of the machine learning artificial intelligence model or models 50, 51, 52 for estimating, at any time, said estimated instantaneous weight Mi of said aircraft 1, or an aircraft of the same type, based on a current set of flight data Jc.

[0129] Furthermore, the predetermined flight phase or phases may also be chosen as a function of a required diversity of the plurality of flights carried out by the user of the aircraft 1, or an aircraft of the same type.

[0130] Moreover, as shown in FIG. 4, carrying out 23 at least one predetermined consistency test may therefore comprise several tests implemented separately from each other. The tests are then carried out 23 if at least one of the tests is validated.

[0131] Therefore, according to one test example, the reference weight Mref may be defined depending on an estimated weight Me of the aircraft 1 based on measurements from several sensors 4.

[0132] This test comprises estimating 29 the estimated weight Me, calculating 30 a difference E between the calculated weight Mc and the estimated weight Me, then checking 31 that the difference E is less than a difference threshold value Se.

[0133] According to another test, the reference weight Mref may be defined depending on a checked theoretical weight Mtv of the aircraft 1. The test then comprises checking 32 the checked theoretical weight Mtv, then checking 33 a parameterization of the checked theoretical weight Mtv.

[0134] According to another test, the reference weight Mref may also be defined depending on a calculated theoretical weight Mtc of the aircraft 1, said calculated theoretical weight Mtc being obtained by a calculation 34 using the weight calculation controller or controllers 5, 15 based on at least one piece of information parameterized with the human-machine interface 7 of the aircraft 1 by the user prior to this consistency test. This test then comprises checking 35 that the information has been parameterized.

[0135] Naturally, the present disclosure is subject to numerous variations as regards its implementation. Although several embodiments are described above, it should readily be understood that it is not conceivable to identify exhaustively all the possible embodiments. It is naturally possible to envisage replacing any of the means described by equivalent means without going beyond the ambit of the present disclosure.