Method for discriminating icing conditions using machine learning

Abstract

One aspect of the invention relates to a computer-implemented method (10) for discriminating icing conditions in the environment of an aircraft, the aircraft carrying an ice accumulation sensor (20). The method (10) comprises, at each measurement cycle of a plurality of measurement cycles: Acquisition (13) of data from the ice accumulation sensor (20) for the current measurement cycle; Classification (14) of the data acquired for the current measurement cycle, by a machine learning algorithm, into a class from a plurality of classes comprising at least: a first class representing a first icing condition in the environment of the aircraft and a second class representing a second icing condition in the environment of the aircraft. Figure to be published with the abstract: Figure 1

Description

Title of the invention: Method for discriminating icing conditions by machine learning TECHNICAL FIELD OF THE INVENTION

[0001] The technical field of the invention is that of aeronautics.

[0002] The present invention relates to a method for discriminating icing conditions. by machine learning and in particular by supervised learning. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] When an aircraft passes through a cloud containing supercooled liquid water particles, these particles will cause the accretion of a layer of ice on certain parts of the aircraft. In order to prevent such a phenomenon from occurring, it is important to detect icing conditions in an environment around the aircraft. Indeed, part 25.1420 of the document "Easy Access Rules for Large Aeroplanes (CS-25)" requires that an aircraft can operate safely in the icing conditions defined by Appendix O of Part 25 of the Code of Federal Regulation 14. Appendix O of Part 25 concerns the presence of supercooled water droplets with a diameter greater than 100 pm in the atmosphere. Appendix C to Part 25 describes the case of supercooled water droplets with a diameter less than or equal to 100 micrometers in the atmosphere.The average diameter of such supercooled water droplets is typically of the order of 10 micrometers. Appendix D to Part 25 considers ice crystals.

[0004] To detect icing conditions, two types of sensors are mainly used: atmospheric sensors and accretion sensors. Atmospheric sensors detect icing conditions by detecting droplets or crystals in the atmosphere. Accretion sensors detect the accretion of frost on a surface.

[0005] For example, an accretion sensor is the sensor described in document FR2970946A1, and a method for detecting icing conditions is described in document FR3074145A1. This sensor is capable of detecting icing conditions without discriminating between them. To do this, the sensor measures the thickness of frost accreted on a target, placed outside the aircraft. A laser module illuminates the target and part of it is reflected there. By measuring the attenuation of the intensity of the laser radiation, the thickness of the frost is calculated, because the frost absorbs part of the laser radiation. In FR3074145A1, the laser module comprises two laser sources of different wavelengths, a laser source whose intensity is more attenuated by solid water and another laser source whose intensity is more attenuated by liquid water. Thus, it is possible to distinguish the attenuation of the laser radiation intensity induced by the presence of liquid water from that induced by the presence of frost. Document FR2008204 describes a method for achieving this discrimination, in a temporal manner. The idea is based on detecting rapid accretions induced by large droplets, by following the evolution of a frost thickness on the sensor. To do this, the bandwidth of the detection electronics must be significantly increased, which induces hardware modifications.

[0006] Currently, many other atmospheric or accretion sensors exist. The document AIR4367 "AIRCRAFT ICE DETECTORS AND ICING RATE MEASURING INSTRUMENTS" from SAE International lists sixteen major types of sensors based on different measurement principles (mechanical, acoustic, thermal, optical, etc.). Despite the diversity of these technologies, only two technologies have demonstrated the ability to discriminate Annex O conditions from other icing conditions: optical interferometry and light scattering. These two technologies are implemented by atmospheric sensors that perform detection on a small volume, which allows them to isolate the signal from large individual droplets. To achieve this, they rely on fast, high-frequency, and sensitive optoelectronic components.Indeed, classification into Annex O conditions depends on the presence of large droplets among the supercooled water droplets. However, whatever the Annex O conditions encountered, the larger the droplets, the rarer they are. Thus, an Annex O conditions / Annex C conditions discriminator must be able to detect a few large droplets in a set of much more numerous small droplets. Systems allowing this are expensive, which makes them incompatible with the current market. In addition, it is necessary to be able to discriminate frost accretion from a single large droplet, from frost accretion from a set of smaller droplets.

[0007] It is therefore appropriate to propose an approach other than the approaches of the prior art. Summary of the invention

[0008] The invention provides a solution to the problems mentioned above, by allowing discrimination of icing conditions between a presence of supercooled water droplets with a diameter greater than 100 pm in the atmosphere and a presence of supercooled water droplets with a diameter less than 100 micrometers in the atmosphere, without having to modify an existing sensor and without using an expensive sensor.

[0009] One aspect of the invention relates to a computer-implemented method of discriminating mination of icing conditions in an environment of an aircraft, the aircraft carrying an ice accumulation sensor, the method comprising, at each current measurement cycle of a plurality of measurement cycles: • Acquisition of data from the ice accumulation sensor for the current measurement cycle, • Classification of the data acquired for the current measurement cycle, by a machine learning algorithm, into one class among a plurality of classes comprising at least: • a first class representative of a first icing condition of the aircraft environment and • a second class representative of a second icing condition of the aircraft environment

[0010] Thanks to the invention, it is possible, by using an existing accretion sensor and without hardware modification, to discriminate between several types of icing conditions in an environment around an aircraft. Thus, there is no need to use an expensive ice level sensor, whether atmospheric or accretionary. In addition, there is no need to replace the accretion sensors of an entire fleet of aircraft.

[0011] In addition to the characteristics which have just been mentioned in the preceding paragraph, the method according to one aspect of the invention may have one or more complementary characteristics among the following, considered individually or according to all technically possible combinations: • The process first includes: • Training a model of the supervised learning algorithm using training data from the ice accretion sensor, the training data being annotated with the first icing condition in the aircraft environment or with the second icing condition in the aircraft environment. • the supervised learning algorithm is chosen from: • logistic regression, • “K nearest neighbors”, • decision tree, • support vector machine, • Random Forest, • AdaBoost, • GradientBoost, • XGBoost, • CatBoost, • neural network. • the first icing condition includes the presence of supercooled drops having a diameter less than or equal to 100 micrometers and the second icing condition includes the presence of supercooled drops having a diameter greater than 100 micrometers. • the classification of the data acquired for the current measurement cycle includes the attribution, to the data acquired for the current measurement cycle, of a value corresponding to the average diameter of the drops included in the aircraft environment, to the maximum diameter of the drops included in the aircraft environment or to the liquid water content of the drops included in the aircraft environment. • The method further comprises an aggregation of the classification classes of each measurement cycle of the plurality of measurement cycles and an overall icing condition is obtained from the aggregation of the classification classes. • obtaining the overall icing condition from the aggregation of the classification classes is carried out by calculating a majority occurrence of a class among the first class and the second classification class of the classification classes of the plurality of measurement cycles. • the classification of the data acquired for the current measurement cycle further comprises the assignment of a probability of belonging to the classification class, and the obtaining of the global icing condition from the aggregation of the classification classes is carried out by calculating an overall probability of each class among the first class and the second classification class for the aggregation of the classification classes of the plurality of measurement cycles. • The method first comprises a step of optimizing a model used in the machine learning method, the optimization comprising a selection of parameters of the model, the parameter selection being carried out by a bottom-up approach or by a top-down approach.

[0012] Another aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, lead the latter to implement the method according to the invention.

[0013] Yet another aspect of the invention relates to a computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to implement the method according to the invention.

[0014] The invention and its various applications will be better understood upon reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0015] The figures are presented for information purposes only and in no way limit the invention. • [Fig.l] shows a schematic representation of the method according to the invention, • [Fig.2] shows a schematic representation of the training step of the method according to the invention, • [Fig.3] shows a schematic representation of an example of an accretion sensor used to implement the method according to the invention, • [Fig.4] shows a schematic representation of an alternative embodiment of the method according to the invention. DETAILED DESCRIPTION

[0016] Unless otherwise specified, the same element appearing in different figures has a single reference.

[0017] [Fig.l] shows a schematic representation of a method for discriminating icing conditions according to the invention.

[0018] The method 10 makes it possible to discriminate icing conditions in the environment of an aircraft. “Discriminate icing conditions” means differentiating icing conditions from among a plurality of classes of icing conditions. Thus, a classification of icing conditions of an environment as belonging to a first class of icing conditions or as belonging to a second class of icing conditions is a discrimination of icing conditions.

[0019] The plurality of icing condition classes may comprise icing condition classes differentiated by a water drop size, by a water drop state, and / or by membership in an icing condition definition comprising several parameters.

[0020] A size of water drops present in the environment of the aircraft is for example defined by a median diameter of the water drops, an average diameter of the water drops, a maximum diameter of the supercooled water drops among all the supercooled water drops in the environment of the aircraft, or any other parameter making it possible to define a size of the water drops in the environment of the aircraft.

[0021] A state of the water drops present in the environment of the aircraft is for example defined by the presence of a majority of supercooled water drops or in the form of crystals, or by the presence of supercooled water drops or in the form of crystals.

[0022] A definition of icing conditions comprising several parameters is for example defined by the various annexes of part 25 of the Code of Federal Regulation 14. For example, a first icing condition may be that defined by Appendix O to Part 25, which includes the presence of supercooled water droplets with a diameter greater than 100 pm in the aircraft environment. For example, a second icing condition may be that defined by Appendix C to Part 25, which includes the presence of supercooled water droplets with a diameter less than or equal to 100 micrometers in the aircraft environment. The average diameter of such supercooled water droplets is generally of the order of 10 micrometers. For example, a third icing condition may be that defined by Appendix D to Part 25, which includes the presence of ice crystals.

[0023] Thus, the method discriminates between several icing conditions when it classifies data representative of a state of the environment of the aircraft as belonging to a first icing condition rather than to a second icing condition.

[0024] The method 10 may be implemented by a computer or by a processor. Preferably, the computer is a microcontroller. By "computer-implemented" is meant that the steps, or at least one step, are executed by at least one computer or processor or any other similar system. Thus, steps are performed by the computer, possibly fully automatically or semi-automatically. In examples, the triggering of at least some of the steps of the method may be performed by user-computer interaction. The level of user-computer interaction required may depend on the level of automation intended and balanced against the need to implement the wishes of the user. In examples, this level may be defined by the user and / or predefined.

[0025] A typical example of a computer implementation of a method is to execute the method with a system adapted for this purpose. The system may comprise a processor coupled to a memory and a graphical user interface ("GUI"), the memory having recorded thereon a computer program comprising instructions for implementing the method. The memory may also store a database. The memory is any hardware adapted for such storage, possibly comprising several distinct physical parts.

[0026] The method 10 comprises at least four steps 11 to 14. A first step 11 is a step of training a classification model. A second optional step 12 is a step of optimizing the trained model. A third step 13 is a step of acquiring data from an ice accretion sensor, and a fourth step 14 is a step of classifying the data acquired from the trained and optionally optimized model.

[0027] The method 10 comprises a first step 11 of training a machine learning algorithm. This step 11 comprises several sub-steps, and is re presented schematically in [Fig.2]. The machine learning algorithm is preferably supervised, but the invention also covers cases where the machine learning algorithm is unsupervised. In such a case, the data are not annotated during the training step 11.

[0028] The first step 11 of training a machine learning algorithm includes creating a classification model, and storing this model.

[0029] The term “machine learning algorithm” means an algorithm capable of estimating a model from a finite number of selected data, then using the estimated model to perform a task by taking unselected data as input. In the invention, the task to be performed is the classification of data from an ice accretion sensor, to obtain an icing condition of the aircraft environment. Such an algorithm is said to be “supervised” when the data selected and provided as input to the algorithm during the training phase are annotated. In the preferred embodiment of the invention, during the training step 11, data already classified, i.e. annotated as belonging to a class, are provided as input to the algorithm. The training data are acquired in a sub-step 111.The training data as well as the classified data are preferably from the same type of ice accumulation sensor 20, shown in [Fig. 3]. The invention can use any ice accumulation sensor 20. An ice accumulation sensor can also be called a frost or ice accretion sensor or a frost accumulation sensor.

[0030] An example of an ice accumulation sensor 20 that can be used to acquire data used in the method 10 is shown schematically in [Fig. 3]. Such a sensor 20, or capture device 20, is preferably fixed on an exterior surface of an aircraft such as a wing, a tailplane, a fuselage, an engine nacelle, or any other location of the aircraft. The aircraft carrying the sensor 20 can be any type of aircraft such as an airplane, a helicopter, a drone.

[0031] With reference to [Fig. 3], the sensor 20 comprises an emitter 21 configured to emit optical radiation, and a receiver 22 configured to receive optical radiation emitted by the emitter 21. In addition, according to the example presented in [Fig. 3], the sensor 20 also comprises a protuberance 23 comprising a target surface 231. The target surface 231 is configured to be diffusing or reflective depending on the geometry of the sensor 20. The target surface 231 makes it possible to return at least a portion of the optical radiation emitted by the emitter 21 to the receiver 22. The protuberance 23 is configured to heat cyclically, making it possible to defrost the target surface 231 at each cycle. The heating of the target surface 231 is preferably carried out by one or more Peltier-type modules.

[0032] The protuberance 23, also called a probe, preferably has an aerodynamic profile. Such an aerodynamic profile is notably defined by the National Advisory Committee for Aeronautics, also known as NACA, an acronym for the English "National Advisory Committee for Aeronautics".

[0033] In particular, in the embodiment illustrated in [Fig. 3], the protuberance 23 has a cylindrical shape which extends perpendicular to an external surface 24 of the aircraft.

[0034] The sensor 20 further comprises a processor 25. The processor 25 is capable of controlling the performance of measurements and / or recovering measurements performed by the various components of the sensor 20, and of creating metrics, in particular metrics related to the heating of the target surface 231, metrics related to the presence of frost on the target surface 231, and metrics related to the quantity, thickness and state of the frost on the target surface 231. All these measurements and metrics are data that can be acquired in sub-step 111 of step 11 of the method 10.

[0035] The data acquired in sub-step 111 of step 11 of method 10 and acquired in step 13 of method 10 come from the sensor 20. By “coming from the sensor 20” is meant data coming directly from the sensor 20 and / or data calculated from data coming directly from the sensor 20. Thus, the data coming directly from the sensor 20 can comprise: voltage and current data of the transmitter 21 in Volts, contrast data at the target surface 231 calculated from the optical radiation received by the receiver 22, accretion velocity or accretion rate data at the target surface 231, for example in centimeters per hour, temperature data, for example the cooling temperature of the target surface 231, and / or the temperature of one or more faces of the Peltier module which cools the target, preferably of the face of the Peltier module opposite the face of the Peltier module which cools the target, for example expressed in degrees Celsius or Fahrenheit, heating power data of the sensor 20 in Watts, and preferably heating power of the target surface 231. As the heating is carried out by resistors, the heating power data includes information linked to the atmosphere and therefore to the environment in which the sensor 20 operates, the heating power depending on the atmospheric conditions to reach a temperature setpoint.

[0036] The acquired data may further include data calculated from data directly from the sensor. These calculated data may include averages, linear regressions and / or curve fitting techniques. (“curve fitting in English”) based on data acquired directly from the sensor 20. During the training step 11, the calculation of this data is carried out in a sub-step 112.

[0037] For example, a curve fitting technique may be used from the voltage data of the receiver 22 in Volts or from the contrast data at the target surface 231 calculated from the optical radiation received by the receiver 22. For this, physical models of the behavior of these quantities must be used. For example, these models may be: • For transmitter voltage data 21: Lh^ — ahi + and M*) = Litâ = “lo + bloe^ with • ahi, a[o, bhi and bho coefficients allowing to take into account the phenomenon of diffusion on the different interfaces encountered by light at low (lo) and high (hi) wavelengths respectively, • Cio and chi coefficients reflecting the absorption phenomenon at low (lo) and high (hi) wavelengths respectively; • More precisely, • Cio = 2 aio IAR / cos(0) where aio is the linear absorption coefficient of solid water at wavelength lo, 0 the angle of incidence of light on the target and IAR the ice accretion rate, • And, • chi=2 ahi IAR / cos(0) where ahi is the linear absorption coefficient of solid water at wavelength hi, 0 the angle of incidence of light on the target and IAR the ice accretion rate. • For contrast data at the target surface 231 ■ (j — Ljo+Lià • Considering the wavelengths of the lasers lo at 1430nm and hi at 1550nm respectively, ahi= 4 aio. Then _ a+be^-ab^^1 _ with a a+be^+a+be^'c*1 la+b^^+e^^) and b coefficients allowing to take into account the phenomenon of diffusion on the different interfaces encountered by the light, considered equal to the wavelengths lo and hi, (with the previous notations: a=aio=ahi and b=bio=bhi) and c a coefficient translating the phenomenon of absorption at the wavelength lo, more precisely, c=Cio=2 aio IAR / cos(0) where a is the linear absorption coefficient of solid water at the wavelength lo, 0 the angle of incidence of the light on the target and IAR the speed of accretion of the ice or Ice Accretion Rate.

[0038] When the machine learning algorithm is supervised, the data acquired for training has been assigned a class, for example by an operator, in an annotation sub-step 113. In the invention, the term “annotating data” means the creation of data comprising the data to be annotated and a label comprising the name of the class assigned to the data. The classes have been chosen from the classes representative of an icing condition, as described previously. Each training data has been annotated for example by an operator or automatically, as acquired in icing conditions defined by Annex O, or as acquired in icing conditions defined by Annex C.Other examples include, but are not limited to, data annotated as acquired during the presence in the environment of the aircraft of water drops with an average diameter of 50 micrometers or data annotated as acquired during the presence in the environment of the aircraft of ice crystals. One or more physical models such as those presented in the preceding paragraph can be used to propose as input to the supervised learning algorithm numerical quantities characteristic of the kinetics of ice accretion at the target surface 231. This allows better classification by the model once trained, because the model then takes into account information derived from data coming directly from the sensor 20, this data not being directly accessible from the sensor 20.

[0039] During the training step 11, the machine learning algorithm therefore receives as input, in a sub-step 114, data, originating from a training data set, the training data comprising at least one data item originating from the sensor 20 and a class label defined for example by an operator according to the formalism explained previously. When the machine learning algorithm is supervised, the training data received are annotated.

[0040] The machine learning algorithm can be, for example, of the “Gradient Boosting” type. “Boosting” is a model aggregation technique, each model having been created during a training step and the weight of each of the models aggregated by the final model being corrected at each training iteration. For example, in the case of decision tree boosting, each of the models is a “weak” decision tree. A model is said to be “weak” when the probability of success on a prediction is slightly higher than that of a random choice. The Boosting technique filters the training data received: the training data that are easy to process are left to the already existing weak models, and other weak models are created to classify the residual training data that the existing models cannot process.At the next training iteration, the residual training data from the previous iteration will not be . more residual since a weak model will have been created to process them. The final model aggregates all the weak models. In the case of Decision Tree Boosting, it is a complex decision tree created from simple decision trees. The weight of each of the weak models in the strong model is re-evaluated at each iteration of the training step based on the classification performance of each of the weak models.

[0041] In a machine learning algorithm, we seek to optimize (maximize or minimize depending on the case) the objective function (according to the Anglo-Saxon term "loss function"). This function compares the output predicted by the algorithm to the expected output (the label). This function is optimized by adjusting the model parameters. In the case of a decision tree boosting algorithm, the parameters are, for example, the number of "weak" trees and their size, the size of the "strong" tree or the weight of each of the weak trees within the strong tree. The term "gradient" in "Gradient Boosting" refers to the use of gradient descent to optimize the objective function. Adding a "weak" tree to process the residual training data must therefore follow a gradient descent of the objective function to optimize it.If adding a weak tree does not amount to performing gradient descent on the objective function, then the weak tree is not added.

[0042] An implementation of the Tree Gradient Boosting algorithm may be used, such as "XGBoost" for "eXtreme Gradient Boosting" described in ["XGBoost: A Scalable Tree Boosting System", Tianqi Chen and Carlos Guestrin, arXiv:1603.02754], "AdaBoost", or "CatBoost".

[0043] In variants, other machine learning algorithms may be used, for example neural networks, simple decision trees, Random Forest, Support Vector Machines (SVM), the k-nearest neighbors method (KNN) or logistic regression. The choice of the optimal model may be carried out by cross-validation.

[0044] When a model has been created, in substep 114, from the training data, the model is saved in substep 115 to be used in the following steps.

[0045] The method 10 comprises a second step 12 of optimizing the model.

[0046] The optional model optimization step 12, which can be performed during the model training step 11, includes a selection of the training data types, also called “parameters”. This selection can be performed according to any parameter selection method, preferably according to one of the three embodiments presented below.

[0047] In a first embodiment, an operator chooses the parameters to be used. For example, the operator can choose the temperature at the target surface 231 and physical quantities from a physical model of the voltage of the receiver 22.

[0048] In a second embodiment, the parameters are chosen via a bottom-up approach, also called "forward stepwise selection". In such a bottom-up approach, the model is first trained from a single parameter among a plurality of parameters, for example parameters A, B, C and D, then a score of the model is calculated. A plurality of scores are thus obtained, that is to say a score for each of the parameters A, B, C and D, and the parameter making it possible to obtain the best score is selected, for example parameter A. Such a score can be for example one or more of the following scores, taken alone or in combination: sensitivity, precision, specificity, the Fl score.A parameter is then added and the model is trained with two parameters: the first parameter selected as having achieved the best score, for example parameter A and each other remaining parameter of the plurality of parameters, i.e. each of parameters B, C and D, in combination with parameter A. A new score of the model is calculated for each set of two parameters comprising parameter A, i.e. a score for the combination of parameters A and B, a score for the combination of parameters A and C, a score for the combination of parameters A and D. The second parameter selected in combination with the first parameter is the parameter, among the remaining parameters of the plurality of parameters B, C and D, allowing to achieve the best new score in combination with parameter A, for example parameter C.If the best new score obtained with A and C is better than the previous score obtained with the single best parameter A, for example because it is higher, the second parameter C is retained with parameter A for further training and classification. If the best new score is worse, no second parameter will be used for further training or classification, and the single parameter A is selected. The process is repeated for the predefined set of parameters, adding one parameter from the remaining parameters B and D at each step if the best new score is better than the previous score. The retained parameter added is the one that, when added, results in the best new score. The predefined set of parameters was selected by an operator, each parameter having a relationship to the icing conditions in the aircraft environment.Training is stopped when the score is no longer improved, that is, when the best score of the new scores is lower than the best score of the previous scores.

[0049] In a third embodiment, the parameters are chosen via an approach top-down, also called "backward stepwise selection". In such a top-down approach, the model is trained using all the parameters in a predefined parameter set, for example, A, B, C and D, and then a model score is calculated. Such a score can be, for example, one or more of the following scores, taken alone or in combination: sensitivity, precision, specificity, the Fl score. A parameter is then removed from the training parameter set and the model is trained with the new subset of parameters. A new model score is calculated. This parameter removal is carried out for each parameter in the predefined parameter set, allowing several scores to be obtained. For the example, we then obtain four scores: one score for the set A, B and C, one score for the set A, B and D, one score for the set A, C and D and one score for the set B, C and D.The best score of the four new scores, for example the score obtained for sets A, B and C is selected. If the best new score, i.e. the score obtained for sets A, B and C, is worse than the previous score, i.e. the score obtained for A, B, C and D, the entire set of training parameters will be used for further training and classification. If the best new score, i.e. the score obtained for sets A, B and C, is better than the previous score, i.e. the score obtained for A, B, C and D, for example because it is higher than the previous score, parameter D, the removal of which led to the best new score, is removed for further training and classification. The process is repeated for the predefined set of parameters, i.e. by removing a parameter at each step if the best new score is better than the previous score.The parameter kept removed is the one that, when removed, results in the best new score. The predefined set of parameters was selected by an operator, with each parameter related to the icing conditions in the aircraft environment. Training is stopped when the score is no longer improved, i.e., when the best score of the new scores is lower than the best score of the previous scores.

[0050] The optimization 12 of the model may include a test step, during which the robustness of the estimated model is tested with test data, coming from the same data set as the training data. The estimated model is provided with unannotated messages as input, and the model's prediction is compared with the expected output. The robustness of the model will then be evaluated automatically and / or by a business expert, i.e. the number of false positives and false negatives must not exceed a certain threshold set by the expert.

[0051] At the end of the training step 11 and the optional optimization step 12, a trained model is stored in a memory, remote or local, of the computer implementing the method 10, and the trained model can be used to classify data of the same type as the data used during training.

[0052] The following steps 13 and 14 of the method are repeated for a plurality of measurement cycles of the accretion sensor 20. A measurement cycle is defined as a predefined time interval, preferably predefined between two de-icings of the target surface 231 of the accretion sensor 20. For example, this time interval has a duration of between 10 and 30 seconds. At each measurement cycle of the sensor 20, steps 13 and 14 are carried out, thus making it possible to obtain at each measurement cycle of the sensor 20 an estimate of the icing conditions in the environment of the aircraft.

[0053] In step 13, data from the ice accumulation sensor 20 are acquired for the current measurement cycle. This acquisition can be carried out by wired or wireless connection between the sensor and the computer implementing the method 10. Step 13 also comprises the calculation of data not directly from the sensor 20, that is to say data calculated from data directly from the sensor 20. To know which data and types of data to acquire, the method 10 can comprise the use of a configuration file indicating the data and parameters selected for training the model, and therefore the data and parameters that the trained model knows how to classify. The data acquisition step 13 thus comprises the acquisition of the same types of data as the training data of the model used to carry out the classification of step 14.

[0054] Step 14 of data classification is then performed by the previously trained machine learning algorithm using the model previously estimated in step 11 and optionally optimized in step 12.

[0055] At each current measurement cycle among the plurality of measurement cycles during which the method is implemented, the machine learning algorithm receives as input the data acquired in step 13 for the current measurement cycle, and classifies them as belonging to one class among a plurality of classes. The plurality of classes comprises at least two classes, among which: • At least one first class representative of a first icing condition and • at least one second class representative of a second icing condition.

[0056] At each measurement cycle, all the data acquired at step 13 are processed at the same time, and are all used to perform the classification. Thus, at each measurement cycle, a single result is obtained, the result being the class into which the data set acquired at step 13 for the current cycle has been classified. Thus, at a given current cycle, a representative class of icing conditions defined in Appendix O can be obtained as a result. For this, the data can be annotated at the output of the supervised learning algorithm with any label re presenting the icing conditions defined in Annex O, for example an “O”. At another given current cycle, a representative class of icing conditions defined in Annex C can be obtained as a result. To do this, the data can be annotated at the output of the supervised learning algorithm with any label representing the icing conditions defined in Annex C, for example a “C”.

[0057] The plurality of classification classes may comprise more than two classes, for example to discriminate between different sizes of supercooled water droplets present in the environment of the aircraft. At the output of the supervised learning algorithm, values ​​may alternatively be assigned to the data, for example average diameter values ​​of the supercooled water droplets in the environment of the aircraft. These values ​​are then representative of an icing condition of the environment of the aircraft. For example, rather than classifying into two classes "Annex C" and "Annex O", it is possible to classify into more than two classes, for example "Annex C", "FZDZ<40", "FZDZ>40", "FZRA<40" and "FZRA>40", with FZDZ from "Freezing Drizzle" and FZRA from "Freezing Rain", and the number corresponding to a water droplet size.

[0058] At the output of the machine learning algorithm, i.e. at the end of the classification 14, a probability that the classified data belongs to the class in which the data has been classified may also be added to the data. For this, the classification algorithm used may be chosen from the algorithms of: Support Vector Classification (SVC), logistic regression, Gaussian process classifier, Random Forest, AdaBoost, GradientBoost, without this list being limiting.

[0059] In an embodiment shown in [Fig.4], the method 10' comprises an additional step 15 compared to the method 10. This step 15 is carried out at each measurement cycle, and takes into account the classification result of the current cycle and the results of the classifications of a plurality of previous measurement cycles. This plurality of previous measurement cycles may comprise a predefined number n of measurement cycles, for example 15 previous measurement cycles.

[0060] In step 15, an aggregation of the results, therefore of the classification classes of step 14, is carried out. For this, all of the classes assigned to the data during the classification 14 for the plurality of cycles preceding the current measurement cycle and for the current measurement cycle are aggregated, and an overall classification class is obtained as output. This makes it possible to obtain a more precise and more reliable icing condition, over a longer time interval.

[0061] The aggregation 15 can be carried out in several ways. A first embodiment of the aggregation comprises the calculation of a statistical quantity on the plurality of classification results, for example a majority occurrence. For example, if, over the last 15 cycles, the data acquired in step 13 have been classified 10 times as representing icing conditions defined by Annex O, then the overall class for these 15 cycles will be the class representing the icing conditions defined by Annex O. In this first embodiment, in the case of a predefined number n of cycles where n is an even number, priority rules must be defined. For example, for greater precautions, in the event of a tie, the most significant icing conditions can be prioritized. For example, it is possible to consider that in the event of a tie, i.e. with a number n for example which is equal to 4 and two predictions “icing conditions of Annex C” and two predictions “icing conditions of Annex O”, the overall class chosen would be the icing conditions defined by Annex O.In this first embodiment, alternatively, when the predicted class corresponds to a value, for example of diameter of supercooled water drops, averages of values ​​can be calculated. A second embodiment of the aggregation comprises the use of the probability assigned to the classification by the supervised learning algorithm. In this second embodiment, the results are weighted by their assigned probability. The overall class can then be the class, among the plurality of aggregation cycles, having the highest probability, or perhaps the class with the greatest number of occurrences among the classes of the plurality of measurement cycles, weighted by their probability.

[0062] The number n of previous cycles aggregated with the current cycle is preferentially chosen by an operator. The larger n is, the longer the time required by step 15 but the higher the accuracy of the result.

[0063] For example, the trained model could be integrated into a sensor on board an aircraft and provide results during flight.

[0064] Tests carried out on the method according to the invention have made it possible to show better performances with the following 8 parameters, and by using, for each parameter, an average, a linear regression, and a curve approximation: • Voltage of the low wavelength laser (Lo) (V), • High Wavelength Laser Voltage (Hi) (V), • Standardized Laser Contrast, • Accretion rate (cm / h), • Temperature of one side of the Peltier module (°C), • Temperature of the other side of the Peltier module (°C), • Peltier module power (W), • Temperature at the back of the protrusion 23 of the sensor 20 (°C).

[0065] The training data accounted for 80% of a selected dataset comprising 2941 samples, and the test data accounted for 20% of the dataset. selected data. A sample is a measuring cycle according to the invention.

[0066] With stratified sampling cross validation, and using the CatBoost® model, the method according to the invention made it possible to obtain an accuracy of 99.46% ± 0.10 (13 samples misclassified out of 2941 samples) with 100 iterations for a classification between the two classes “Annex O” and “Annex C”.

[0067] With stratified cross-validation with non-overlapping groups, and using the CatBoost® model, the method according to the invention made it possible to obtain an accuracy of 96.60% ± 0.89 (82 misclassified samples out of 2941 samples) with 100 iterations for a classification between the two classes “Annex O” and “Annex C”.

Claims

Claims

1. A computer-implemented method (10) for discriminating icing conditions in an aircraft environment, the aircraft carrying an ice accumulation sensor (20), the method (10) comprising, at each current measurement cycle of a plurality of measurement cycles: - Acquisition (13) of data from the ice accumulation sensor (20) for the current measurement cycle, - Classification (14) of the data acquired for the current measurement cycle, by a machine learning algorithm, into a class from among a plurality of classes comprising at least: • a first class representative of a first icing condition of the aircraft environment and • a second class representative of a second icing condition of the aircraft environment.

2. Method (10) according to the preceding claim comprising beforehand: - Training (11) a model of the supervised learning algorithm from training data from the ice accumulation sensor (20), the training data being annotated with the first icing condition of the aircraft environment or with the second icing condition of the aircraft environment.

3. Method (10) according to one of the preceding claims according to which the supervised learning algorithm is chosen from: - logistic regression, - “K nearest neighbors”, - decision tree, - support vector machine, - Random Forest, - AdaBoost, - GradientBoost, - XGBoost, - CatBoost, - neural network.

4. Method (10) according to one of the preceding claims according to which the first icing condition comprises the presence of supercooled drops having a diameter less than or equal to 100 micrometers and the second icing condition comprises the presence of supercooled drops having a diameter greater than 100 micrometers.

5. Method (10) according to one of the preceding claims, according to which the classification of the data acquired for the current measurement cycle comprises the attribution, to the data acquired for the current measurement cycle, of a value corresponding to the average diameter of the drops included in the environment of the aircraft, to the maximum diameter of the drops included in the environment of the aircraft or to the liquid water content of the drops included in the environment of the aircraft.

6. A method (10) according to one of the preceding claims further comprising an aggregation (15) of the classification classes of each measurement cycle of the plurality of measurement cycles and wherein an overall icing condition is obtained from the aggregation (15) of the classification classes.

7. Method (10) according to claim 6 according to which the obtaining of the global icing condition from the aggregation (15) of the classification classes is carried out by calculating a majority occurrence of a class among the first class and the second classification class of the classification classes of the plurality of measurement cycles.

8. The method (10) of claim 6 wherein the classification (14) of the data acquired for the current measurement cycle further comprises assigning a probability of belonging to the classification class, and wherein obtaining the overall icing condition from the aggregation of the classification classes is performed by calculating an overall probability of each class among the first class and the second classification class for the aggregation of the classification classes of the plurality of measurement cycles.

9. Method (10) according to one of the preceding claims comprising beforehand a step of optimizing (12) a model used in the machine learning method, the optimization (12) comprising a selection of parameters of the model, the parameter selection being

10. carried out by a bottom-up approach or by a top-down approach. Computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the method (10) according to one of claims 1 to 9.

11. A computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the method (10) according to one of claims 1 to 9.

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