Method for predictive control of the health of a turbine engine from a reduced set of data

Abstract

One aspect of the invention relates to a method for predictively monitoring the health of an aircraft engine, comprising: Obtaining at least two sets of continuous data each comprising altitude data, Synchronizing the two sets of data to obtain a single set of synchronized data, from the altitude data of each set of data, the single set of synchronized data comprising data of: synchronized altitude, ambient temperature, low pressure regime, aircraft mach, Estimating the position of a throttle from the synchronized ambient temperature and low pressure regime data, Generating a maneuver file comprising at least the estimated throttle position, altitude and mach data, Simulating the engine taking as input the maneuver file and providing as output an engine health indicator. Figure to be published with the abstract: Figure 2

Description

Title of the invention: Method for predictive control of the health of a turbine engine from a reduced set of data 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 predictive monitoring of the health of a tur bomoteur and in particular such control from a reduced set of data. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] Predictive health monitoring in aeronautics, also abbreviated as "PHM" (from the English "Predictive Health Monitoring"), uses sensor and analysis data, whether data from an aircraft or data from an aircraft engine, then uses them to generate indicators and alerts on the health and usage status of the engine. These indicators and alerts are sent to an operator, in order to anticipate the future maintenance needs of an aircraft or an aircraft system, for example to carry out predictive maintenance. Such predictive control makes it possible in particular to optimize maintenance programs, reduce unplanned downtime and improve the availability and reliability of aircraft.

[0004] [Fig.l] shows a schematic representation of a predictive health monitoring system for an aircraft and its engine.

[0005] The aircraft 1 comprises a turbine engine 11 and a set of sensors 12, for example altitude and speed sensors.

[0006] The turbojet engine 11 itself comprises a plurality of sensors 111 and 112, for example sensors for the temperature of the turbojet engine 11 and for engine speeds.

[0007] These data are collected by a computer 13, which analyses them on the one hand, and transmits some of them on the other hand, for example via a communication system 14 of the aircraft 1. These data are then transmitted, for example by satellite 2, to a ground data analysis system 3 belonging to the manufacturer of the aircraft 1, and to a ground data analysis system 4 belonging to the manufacturer of the engine 12.

[0008] The ground systems 3 and 4 perform the predictive health control functions of the aircraft 1 and its turbine engine 11 via algorithms that they implement. Such algorithms use as input data: • Flight data from aircraft 1 and its engine 11 in order to issue an alert in the event of early warnings or detection of failures, • Background data on the use of engine 11 in operation.

[0009] The engine data acquired in flight are generally collected and stored in the memories of devices on board the engine 11, of the computer (FADEC of English: "Full Automatic Digital Engine Control") or engine monitoring systems 11 ("Engine Monitoring System"). This data can then be transmitted to one or more of the ground data processing systems 3 and 4 for analysis. In particular, it can be transmitted in real time to one of these systems for real-time analysis, allowing a rapid response in the event of faults being detected on the engine 11. The data can, for example, be visualized in the form of graphs, dashboards and reports to facilitate analysis.

[0010] Engine data acquired in flight may be collected punctually or as continuous data.

[0011] Point-collected data is data collected at specific times or in relation to one or more events. This data is often used to monitor engine performance trends and identify potential long-term engine equipment problems. The volume of point-collected data is small, corresponding to a few kilobytes of data per flight.

[0012] Continuous data, on the other hand, is collected continuously throughout the duration of the flight. This data can be used to monitor operating parameters in real time and identify potential problems with the engine 11 equipment quickly. Continuous data is larger than point data and can correspond to several megabytes or even several tens of megabytes of data per flight.

[0013] Both types of data have their usefulness and relevance depending on the use made of them and the specific needs. The choice between data collected punctually and continuous data will depend on the constraints of the communication system 14 of the aircraft 1, in particular the bandwidth available for transmitting the data and the data storage capacity on board the aircraft 1.

[0014] Point-collected data requires less storage capacity and less bandwidth for transmission because it is collected at regular intervals or on an event basis. However, point-collected data may not capture important transient events that do not occur at the time the data is collected.

[0015] Continuous data, on the other hand, requires more storage capacity and more bandwidth for transmission because it is collected continuously. However, it allows real-time monitoring of operating parameters and rapid detection of potential faults on the engine 11.

[0016] As a result, the flight data used as input to the predictive health control algorithms of the engine 11 are limited, namely: • In number of parameters stored and sent to ground analysis systems 3 and 4, due to the limitation of the memory size of the electronic boxes on board the engine, the size of which is defined during the design phase. • In frequency of acquisition of this data, because when sending the data, it is essential to limit the use of bandwidth.

[0017] In addition, for some engine applications, necessary input data for predictive health control algorithms is unavailable. It would be necessary to generate this missing data. Similarly, for new programs, there is no flight data to verify the proper functioning of the predictive health control algorithms before their implementation and integration into ground systems.

[0018] Predictive health control algorithms exploit continuous flight data. However, all of the flight data relevant to the exploitation of predictive health control algorithms are not always available. Indeed, the bandwidth for transmission and the storage capacity do not generally allow access to all of the data and it is then necessary to make a selection among the available data. For example, it is common to store and transmit only the consolidated and selected data, and generally only the data from the active channel, that is to say only the data from the computer channel which commands and controls the engine.

[0019] These problems imply the use of other types of data for the validation of predictive health control algorithms.

[0020] One solution is to manually create datasets, which is laborious and not very representative of reality, for example by using data from another engine or using plausible values ​​created by hand.

[0021] Another solution is to use bench data, which is more realistic but does not completely represent real flight conditions.

[0022] Finally, yet another solution consists of using a simulation model of the engine to simulate data, this data simulated from real data then being used by the predictive control algorithm of the engine to detect failures or events on the engine, and to be able to take corrective actions.

[0023] A simulation model of the engine makes it possible to test the engine control software used in real conditions, with data which digitally simulate, via simulation models, the operation of the aircraft 1 and its engine 11 through its sensors 111, 112 and 12, actuators, and its thermodynamic environment.

[0024] The objective of the simulation model is to be able to simulate the behavior of the system in many situations. Thus, events occurring in flight in real situation can be reproduced in simulation for investigation.

[0025] The user of the simulation model, for example implemented by the ground system 4, can interact with the inputs of the model to simulate different flight configurations, for example joystick movements of the pilot, the position of the landing gear of the aircraft, or the position of the aircraft in the flight envelope (altitude, speed, etc.): this is called a 'maneuver'. Once the maneuver is completed, the user has access to a recording of all the variables of the control software but also of all the other models present in the simulation model of the engine 11.

[0026] These engine simulation models 11 require a minimum set of continuous data, also called “key parameters”, to be simulated. The key parameters for performing a simulation of the engine 11 via the engine model are altitude, mach, and throttle position. This data is necessary in order to create a maneuver file which is used as input to the simulation of the engine 11.

[0027] As explained previously, the flight data of the engine 11, transmitted to the ground to exploit the predictive health control algorithms of the engine 11, generally do not contain the throttle position. It is therefore not possible to exploit the simulation model to simulate and then visualize the variables of the engine 11.

[0028] Finally, the data generated by the engine simulation model are stored in a digital workspace of limited size, and this for all the variables of the model. Such a digital workspace is for example the “Workspace” of the “Matlab®” software. The model containing several hundred variables and the Matlab Workspace being limited, it is then only possible to process missions of a few tens of minutes at most, which is extremely limiting.

[0029] There is therefore a need to propose a solution resolving the drawbacks of the state of the art, and in particular allowing predictive control of the health of an aircraft engine with less data than in the state of the art. Summary of the invention

[0030] The invention offers a solution to the problems mentioned above, by making it possible to carry out predictive control of the health of an aircraft engine by simulating it with a reduced data set compared to the state of the art.

[0031] One aspect of the invention relates to a computer-implemented method of predictively monitoring the health of an aircraft engine, the method comprising: • Obtaining at least two sets of continuous data, the two sets of data being associated with the same flight of the aircraft and being sampled at different sampling frequencies, each set of data among the two data sets including altitude data, • Synchronization of the two data sets to obtain a single synchronized data set, the synchronization being carried out from the altitude data of each data set among the two data sets obtained, the single synchronized data set comprising data: • synchronized altitude, • aircraft engine ambient temperature, • low pressure regime of the aircraft engine, • aircraft mach, • Estimation of the position of an aircraft throttle lever from the altitude, ambient temperature and low pressure engine speed data of the aircraft engine included in the single synchronized data set, • Generation of a maneuver file, the maneuver file including at least the estimated throttle position, synchronized altitude and aircraft mach data, • Simulation of an aircraft engine operation during the flight of the aircraft, by an aircraft engine simulation model taking the maneuver file as input, the simulation model providing at least one flight data item representing the flight of the aircraft as output, • Obtaining at least one aircraft engine health indicator from at least one flight data. •

[0032] Thanks to the invention, it is possible to simulate the operation of an aircraft during a flight without having position data from the aircraft joystick. This makes it possible in particular to carry out predictive control of the health of the aircraft with a reduced quantity of data transmitted from the aircraft to the ground.

[0033] 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: • estimating the aircraft throttle position from the aircraft engine low pressure and ambient temperature data included in the single synchronized data set comprises: • Constructing a matrix comprising a plurality of aircraft engine low pressure regimes calculated as a function of altitude, ambient temperature and throttle position, the matrix comprising a plurality of vectors, each vector corresponding to a predefined joystick position within a predefined joystick position interval, each vector being separated from an adjacent vector by a predefined joystick position step, each vector comprising a low pressure regime calculated per sample of the single synchronized data set, • Search, for each sample of the unique set of synchronized data, in the vector of the constructed matrix corresponding to the sample, for the calculated low pressure regime closest to the low pressure regime included in the unique set of synchronized data, and addition, in the unique set of synchronized data, of the position of the predefined lever corresponding to the calculated low pressure regime closest to the low pressure regime included in the unique set of synchronized data. • the low pressure regime of the aircraft engine is calculated from the following equation: xn2 = f(alt, T2, PL A) • With xn2 the low pressure regime of the aircraft engine, ait the altitude of the aircraft, T2 the ambient temperature of the aircraft engine, PLA the position of the aircraft throttle, and f a throttle law depending on the aircraft and a predefined engine. • the estimation of the position of the aircraft control lever further includes an estimation of ignition of an afterburner of the aircraft engine from the altitude of the aircraft and a change in the quantity of fuel of the aircraft. • at least one of the two continuous data sets obtained includes aircraft engine ignition data and wherein the aircraft engine afterburner ignition estimate takes into account a number of aircraft engine afterburner ignitions and an aircraft engine afterburner operating time during the aircraft flight. • the method further comprises: • comparison of the health indicator to a decision threshold, • issue an alarm if the health indicator exceeds the threshold of decision.

[0034] Another aspect of the invention relates to a system configured to implement a method according to the invention, the system comprising at least one ground analysis system and at least one aircraft configured to send the two sets of data continues to the ground analysis system, the ground analysis system comprising a simulation model including: • A sensor module, • An aircraft module, • A regulation module, • An actuator module, • A thermodynamic module.

[0035] Yet another aspect of the invention relates to a computer program product comprising instructions which, when the program is executed on a computer, cause the latter to implement the steps of the method according to the invention.

[0036] 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 steps of the method according to the invention.

[0037] The invention and its various applications will be better understood upon reading the description which follows and the examination of the figures which accompany it. BRIEF DESCRIPTION OF THE FIGURES

[0038] The figures are presented for information purposes only and in no way limit the invention. [Fig.l] shows a schematic representation of a prior art aircraft predictive health monitoring system, [Fig.2] shows a schematic representation of a method for predictive health monitoring of an aircraft according to the invention, [Fig.3] shows a graph representing the low pressure speed of the aircraft engine as a function of the throttle position, [Fig.4] shows a schematic representation of a step of the method according to the invention, [Fig.5] shows a schematic representation of a matrix resulting from a step of the method according to the invention, [Fig.6] shows a schematic representation of a simulation model used in the method according to the invention. DETAILED DESCRIPTION

[0039] Unless otherwise specified, the same element appearing in different figures has a single reference.

[0040] [Fig. 2] shows a schematic representation of an embodiment of a method for predictive health monitoring of an aircraft according to the invention. The method 5 of [Fig. 2] comprises at least a plurality of steps 51 to 55.

[0041] A method for predictive health monitoring of an aircraft according to the invention is implemented by computer. 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, for example a microcontroller. Thus, steps are performed by the processor, possibly fully automatically or semi-automatically. In examples, the triggering of at least some of the method steps may be carried out by user-computer interaction. The required level of user-computer interaction may depend on the intended level of automation and balanced against the need to implement the user's wishes. In examples, this level may be user-defined and / or predefined.

[0042] 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.

[0043] The method 5 comprises eight steps 51 to 58. The computer implementing the method 5 is for example a ground analysis system 3 or 4 of the prior art, for example configured for this implementation. Thus, the predictive health monitoring system of the aircraft 1 of the prior art requires very little adaptation: it is sufficient to configure a computer on the ground differently. The method 5 makes it possible to carry out a predictive health monitoring of the aircraft from a reduced set of data, that is to say without having the joystick data of the aircraft 1 available to the ground computer. The joystick data of the aircraft 1 represent the different positions of a control joystick of the aircraft 1. Such positions are between a minimum joystick position and a maximum joystick position.

[0044] The method 5 comprises a first step 51 of obtaining at least two sets of continuous data. The two sets of data obtained are continuous data obtained during the same flight of the aircraft 1, a flight being between a takeoff and a landing at a given time and date. “Continuous data” means data acquired throughout the duration of a flight, continuously, i.e. by predefined sampling steps, for example every second. The two sets of continuous data obtained in step 51 are sampled at different sampling frequencies.In step 51, the at least two sets of continuous data are "obtained", that is to say they are received by the computer implementing the method 5, for example following the sending of a request by the computer or automatically, by configuration of the aircraft 1, which sends continuously or at the end of the flight the continuous data captured during the flight to . the computer.

[0045] Each set of data obtained comprises at least one altitude data item of the aircraft 1, and at least one other data item different for each of the data sets. For example, a first set of data among the data sets obtained comprises, in addition to the altitude data item, Mach data of the aircraft 1 during the flight and the quantity of fuel remaining in the tank of the aircraft 1. A second set of data among the data sets obtained comprises, for example, in addition to the altitude data item, ambient temperature and low pressure regime data of the engine 11 of the aircraft 1. These two sets of continuous data come from two different data sources, for example the first set of continuous data obtained comes from the sensors 12 included in the aircraft 1 and the second set of continuous data obtained comes from the sensors 111 and 112 of the engine 11 of the aircraft 1.These two data sets therefore have different sampling frequencies. In other embodiments of the invention, more than two unsynchronized continuous data sets are obtained in step 51 of the method 5 according to the invention.

[0046] The method 5 therefore comprises a second step 52 of synchronizing the two sets of data to obtain a single set of synchronized data. The synchronization is carried out from the altitude data of each set of data obtained, that is to say by using the altitude data, included in each of the sets of data obtained in step 51, as a reference for synchronizing the two sets. For example, the two sets can be synchronized using an interpolation from the altitude data, the single set of synchronized data taking the sampling frequency of one or the other of the sets of data obtained, or a different sampling frequency. Preferably, the set of synchronized data then comprises data: • of synchronized altitude, • aircraft engine ambient temperature, • low pressure regime of the aircraft engine, • aircraft mach.

[0047] At the end of step 52, all of these data are synchronized into a single set of synchronized data, i.e. associated with the same altitude reference. For example, a piece of aircraft Mach data captured at a first altitude will be associated with a piece of aircraft engine low pressure speed data captured at the same first altitude.

[0048] Since the data obtained in step 51 do not include controller data from the aircraft 1, the method 5 then comprises a step 53 of estimating the position of a controller of the aircraft. This estimation is carried out from the data altitude, ambient temperature and low pressure regime of the engine 11 of aircraft 1 included in the single set of synchronized data resulting from step 52. This step 53 is carried out from the equation:

[0049] xn2 = fiait, T2, PLA)

[0050] With xn2 the low pressure regime of the engine 11 of the aircraft 1, i.e. the altitude of the aircraft 1, T2 the ambient temperature of the engine 11 of the aircraft 1 and PLA the throttle position of the aircraft 1. Such an equation is called "throttle law". An example is shown in [Fig.3], which shows a graph representing the low pressure regime of the engine 11 of the aircraft 1 as a function of the throttle position PLA, varying between PLAmin and PLAmax.

[0051] [Fig.4] shows a schematic representation of step 53 of method 5, which comprises at least two sub-steps 531 and 532, and an optional sub-step 533.

[0052] Thus, step 53 comprises a first sub-step 531 of constructing a matrix ML. The matrix M1 makes it possible to represent a low pressure regime calculated as a function of the lever position.

[0053] For this, the matrix M1 comprises for example a line (or a column) per joystick position between the minimum joystick position PLAmin and the maximum joystick position PLAmax, the interval between the minimum joystick position PLAmin and the maximum joystick position PLAmax being the actual travel of the aircraft joystick 1. The joystick position difference between each adjacent line is a predetermined joystick position step x. For example, between PLAmin and PLAmax, there are p joystick position values ​​separated by a step x.

[0054] For each row of the matrix M1, the low pressure regime of the engine 11 is calculated, using the throttle law, from the altitude of the aircraft 1 and the ambient temperature of the engine 11 of the aircraft 1 synchronized, that is to say included in the unique set of synchronized data resulting from step 52, and from the throttle position PLA corresponding to the row. The matrix M1 has a number of columns (or respectively rows) equal to the number of samples of the unique set of synchronized data resulting from step 52, for example N samples. Thus, a matrix is ​​obtained, represented in [Fig.5], representing a low pressure regime xn2_x calculated as a function of a throttle position PLAx and an altitude Altx and an ambient temperature T2_x.

[0055] Step 53 comprises a second sub-step 532 of searching in the matrix M1, for each sample of the unique set of synchronized data, for the calculated low pressure regime included in the matrix M1 closest to the low pressure regime included in the unique set of synchronized data. For this, each calculated low pressure regime value xn2 of the column of the matrix M1 corresponding to the sample in which the low pressure regime is included is scanned and the closest value to the calculated low pressure regime, among the calculated low pressure regime values ​​of the column, is selected. The lever position PLAx corresponding to the selected low pressure regime value xn2x is then the lever position retained for this sample. This column reading is repeated for each sample of the unique set of synchronized data and therefore for each column of the matrix M1. At the end of substep 532, the unique set of synchronized data then comprises an additional lever position data item synchronized with the other data contained in the set, for each of its samples.

[0056] In a complementary embodiment, step 53 of the method 5 comprises an optional step 533 of estimating the ignition of the afterburner. This step is optional because it only applies to engines comprising afterburner. Afterburner is the combustion of fuel behind the turbine of the engine 11 of the aircraft 1, in the exhaust gases of the engine, thus increasing the thrust of the engine. Engines equipped with afterburner thus have a modified throttle law, because the value of the low pressure regime is modified by the throttle only over a given range included in the interval [PLAmin;PLAmax]. Outside this given range, preferably beyond a predetermined threshold depending on PLAmax, the value of the low pressure regime xn2 is no longer linked to the throttle position but to the afterburner.This optional sub-step 533 allows engines comprising an afterburner to be taken into account, by including an estimation of the state of the afterburner. The afterburner takes either an “off” state or an “on” state. The estimation of the state of the afterburner is carried out from the altitude and changes in the quantity of fuel. Indeed, the altitude and the instantaneous fuel consumption make it possible to estimate whether the afterburner is on or not, because it is possible to notice excess fuel consumption in the event of the afterburner being on. The estimation of the state of the afterburner makes it possible to determine whether the throttle position is beyond the predetermined threshold depending on PLA_max.It is also possible to consolidate this estimate from the number of ignitions of the afterburner of the engine 11 of the aircraft 1 and the operating time of the afterburner during the flight, these data must then be included in at least one of the sets of continuous data obtained in step 51. This estimate of the state of the afterburner is then combined with a vector comprising all of the samples of throttle position estimated in sub-step 532 to obtain a consolidated throttle position vector.

[0057] The method 5 further comprises a step 54 of generating a maneuver file. The maneuver file comprises at least the synchronized altitude and mach data of the aircraft and the estimated joystick position data, op- tionally consolidated at step 533.

[0058] It is then possible to carry out a simulation of the engine 11 of the aircraft 1, at a simulation step 55, from the maneuver file thus generated.

[0059] For this purpose, a simulation model is used. Such a simulation model is shown in [Fig.6].

[0060] The simulation model 60 of [Fig.6] comprises a plurality of modules: a sensor module 61, an aircraft module 62, a regulation module 63, an actuator module 64 and a thermodynamic module 65.

[0061] The sensor module 61 comprises a simulation model configured to simulate the operation of the sensors having captured the data obtained in step 51 and included in the maneuver file.

[0062] The aircraft module 62 comprises a simulation model configured to simulate the aircraft, in particular the position of the joystick, the flight envelope including the altitude, the speed, the pressure and the ambient temperature and the aircraft messaging of the aircraft.

[0063] The regulation module 63 models the regulation software of the two channels of the computer controlling the aircraft engine. For this, the regulation software takes as input data from the sensor module 61, the aircraft module 62, and the actuator module 64. The regulation software manages, as output, the actuators 64 by providing commands intended for actuators to the actuator module 64. Such commands include for example a modification of the position of the variable geometries of the engine 11.

[0064] The actuator model 64 simulates certain actuators of the engine 11 of the aircraft 1, in particular the positions of the variable geometries of the engine 11 of the aircraft 1.

[0065] Finally, the thermodynamic module 65 models, via a simulation model, the thermodynamics of the aircraft 1 and in particular of the engine 11 of the aircraft 1. This thermodynamic module takes actuator data as input and provides data linked to the engine 11 of the aircraft 1 as output, which it transmits to the sensor module 61. The sensor module 61 formats this data to simulate the capture of this data in real conditions on the engine 11 of the aircraft 1.

[0066] In the model 60, the altitude and mach data of the maneuver file are provided as input to the aircraft module 62, and the throttle position data are provided as input to the actuator module 64. The simulation model 60 then provides as output simulated flight data representing the flight of the aircraft 1 during which the two sets of received data were captured.

[0067] These simulated flight data are then used, in a step 56, by a predictive engine health control algorithm to obtain at least one health indicator of the engine 11 of the aircraft 1, for example an abnormality score. To monitor a aircraft engine, it is known to form an indicator which is characteristic of a degradation of the aircraft engine. This indicator is known to those skilled in the art under the name of abnormality score. Conventionally, an abnormality score is formed from measurements of physical parameters of the aircraft engine such as, for example, an engine speed, a geometric position, a quantity of fuel, a temperature, etc. The abnormality score is characteristic of the degree of damage of the degradation. Preferably, an abnormality score is formed for each flight of the aircraft.

[0068] To determine whether the aircraft engine is actually degraded, the method for monitoring the health of the engine may for example comprise a step 57 of comparing the health indicator with a decision threshold, the health indicator being for example an abnormality score obtained for a given flight of the aircraft and a step 58 of issuing an alarm in the event of exceeding the decision threshold. Thus, by monitoring the evolution of the abnormality score, it is possible to detect whether the degree of degradation increases and therefore it is possible to anticipate the risk of failure of the aircraft engine and improve the management of maintenance operations. The health indicator is thus used to alert an operator of maintenance to be planned, to automatically schedule upcoming maintenance, to avoid an identified failure of the engine 11 of the aircraft 1.

[0069] The simulation model is preferably adapted to be implemented via the Matlab® software, and preferably comprises at least one module for selecting parameters to be recorded, by a user. Such a module can be used by a user via a human-machine interface, for example included in the computer implementing the method according to the invention. Once the parameters have been selected, during the simulation 55, only these parameters are recorded regularly, for example every minute, in a storage file. Such a file is for example a binary file. The parameter selection module can then be configured to import, at the end of the simulation 55, the parameters stored in the binary file to the Matlab® workspace, also called the Workspace®.

Claims

1. Claims A computer-implemented method (5) for predictive health monitoring of an aircraft (11) engine, the method comprising: - Obtaining (51) at least two sets of continuous data, the two sets of data being associated with the same flight of the aircraft (1) and being sampled at different sampling frequencies, each set of data among the two sets of data comprising an altitude data (ait), - Synchronization (52) of the two sets of data to obtain a single set of synchronized data, the synchronization (52) being carried out from the altitude data (ait) of each set of data among the two sets of data obtained, the single set of synchronized data comprising data: • of synchronized altitude (ait), • ambient temperature (T2) of the aircraft engine (11), • low pressure regime (xn2) of the aircraft engine (11), • of the aircraft's mach (1), - Estimation (53) of the position of a joystick of the aircraft (1) from the altitude, ambient temperature (T2) and low pressure regime (xn2) data of the aircraft engine (11) included in the single set of synchronized data, - Generation (54) of a maneuver file, the maneuver file comprising at least the estimated joystick position (PLA), synchronized altitude (ait) and mach data of the aircraft (1), - Simulation (55) of an operation of the aircraft engine (11) during the flight of the aircraft (1), by a simulation model (60) of the aircraft engine (11) taking as input the maneuver file, the simulation model (60) providing as output at least one flight data representing the flight of the aircraft, - Obtaining (56) at least one health indicator of the aircraft engine (11) from the at least one flight data.

2. Method (5) according to the preceding claim according to which the estimation (53) of the position of the joystick of the aircraft (1) from the ambient temperature (T2) and low pressure regime (xn2) data of the aircraft engine (11) included in the single set of synchronized data comprises: - Construction (531) of a matrix comprising a plurality of low pressure regimes (xn2) of the aircraft engine (11) calculated as a function of the altitude (ait), the ambient temperature (T2) and the position of the lever (PLA), the matrix comprising a plurality of vectors, each vector corresponding to a predefined position of the lever (PLA) in a predefined range of lever positions, each vector being separated from an adjacent vector by a predefined lever position step (p), each vector comprising a low pressure regime (xn2) calculated per sample of the single set of synchronized data, - Search (532), for each sample of the unique set of synchronized data, in the vector of the constructed matrix (531) corresponding to the sample, for the calculated low pressure regime (xn2) closest to the low pressure regime (xn2) included in the unique set of synchronized data, and addition, in the unique set of synchronized data, of the predefined lever position (PLA) corresponding to the calculated low pressure regime (xn2) closest to the low pressure regime (xn2) included in the unique set of synchronized data.

3. Method (5) according to the preceding claim according to which the low pressure regime (xn2) of the aircraft engine (11) is calculated from the following equation: xn2 = fiait, T2, PLA) With xn2 the low pressure regime of the aircraft engine (11), ait the altitude of the aircraft (1), T2 the ambient temperature of the aircraft engine (11), PLA the position of the throttle of the aircraft (1), and f a throttle law depending on the aircraft (1) and a predefined engine (11).

4. Method (5) according to one of claims 2 or 3 according to which the estimation (53) of the position of the joystick of the aircraft (1) further comprises an estimation (533) of ignition of an afterburner of the aircraft engine (11) from the altitude of the aircraft (1) and a change in the quantity of fuel of the aircraft (1).

5. Method (5) according to claim 4 according to which at least one of the two continuous data sets obtained comprises ignition data of the afterburner of the aircraft engine (11) and according to which the estimation of ignition of the afterburner of the aircraft engine (11) takes into account a number of ignitions of the afterburner of the aircraft engine (11) and an operating time of the afterburner of the aircraft engine (11) during the flight of the aircraft (1).

6. Method (5) according to one of the preceding claims further comprising: - comparison (57) of the health indicator with a decision threshold, - emission (58) of an alarm if the health indicator exceeds the decision threshold.

7. System configured to implement a method (5) according to one of the preceding claims, the system comprising at least one ground analysis system (3, 4) and at least one aircraft (1) configured to send the two sets of continuous data to the ground analysis system (3, 4), the ground analysis system (3, 4) comprising a simulation model (60) comprising: - A sensor module (61), - An aircraft module (62), - A regulation module (63), - An actuator module (64), - A thermodynamic module (65).

8. Computer program product comprising instructions which, when the program is executed on a computer, cause the latter to implement the steps of the method (5) according to one of claims 1 to 6.

9. A computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method (5) according to one of claims 1 to 6.

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