Method of monitoring the health of an aircraft engine

Abstract

One aspect of the invention relates to a method for monitoring a health x of an aircraft engine. The method notably comprises operations of: Acquiring (10) a set of operational points, Determining (20) a reduced set of operational points to a reduced non-observable space, the determination comprising: Defining (22) a representation H of the set of operational points, Defining (23) a quantification of the information provided by the set of operational points, Determining (24) a solution by characterizing an observability of the aircraft engine relative to the set of operational points, Selecting (25) the solution and the associated operational points minimizing a non-observability of the aircraft engine, and Diagnosing (30) the health of the aircraft engine using the model inversion method. Figure to be published with the abstract: Figure 1

Description

Title of the invention: Method for monitoring the health of an aircraft engine 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 monitoring the health of an aircraft engine and in particular such monitoring from a reduced set of sensors. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] Methods for monitoring the health of an aircraft engine in aeronautics use measurement data from sensors and / or analysis data. This data may be data from an aircraft or data from an aircraft engine. These monitoring methods then use this data to generate indicators and alerts on the health and usage status of the engine. These indicators and alerts can be sent to an operator, in order to better manage the use of the aircraft engine. For example, it is possible to anticipate the future maintenance needs of an aircraft or an aircraft system. Such monitoring makes it possible in particular to optimize maintenance programs and / or reduce unplanned downtime and / or improve the availability and reliability of aircraft.

[0004] In one example, the aircraft engine is for example a turbomachine and the method for monitoring the health of the turbomachine comprises monitoring modular parameters such as the efficiencies and flow rates of different compressors and / or turbines of the turbomachine. Several conventional methods exist such as thermodynamic analysis, “gas path analysis” in English, and / or Bayesian filtering on variables of interest to determine the health of the turbomachine.

[0005] When an aircraft engine, having given health parameters, is observed through sensors, it is possible that the system of equations modeling the relationship between the health of the aircraft engine and the measurement data for a given observation, does not allow to differentiate between several sets of health parameters of the aircraft engine. In other words, the same observation by sensors can lead to several solutions for the health parameters of the aircraft engine. This is a known problem in the field of health monitoring: the problem of non-observability. The definition of observability of a system is the ability to measure the internal states of a system by examining what it produces. A system is considered "observable" if its current state can be estimated only by using the output information, namely the data from the sensors. In a way In common practice, the non-observability problem is either ignored, addressed retrospectively, or addressed by adding sensors and / or operating point recordings. An operating point, also called an operational point, corresponds to an aircraft engine operation for predetermined inputs provided to the aircraft engine.

[0006] A first solution to this problem is to increase the number of sensors used in order to obtain a greater number of measurement data and therefore operating points. However, this solution has several drawbacks. First of all, adding measurement sensors presents a significant financial cost and adds additional constraints on the system such as additional weight to the system and / or additional maintenance tasks to manage these sensors.

[0007] With data recordings, it is possible to record a large number of operating points for transmission. However, obtaining and transmitting all the operating points is costly in terms of resources, particularly energy. Indeed, this increases, for example, the calculation time and the cost of transmitting and storing the data. It is therefore necessary to choose a limited number of relevant operating points to transmit. Thus, a second solution consists of reducing the unobservable space of the health of the aircraft engine without increasing the number of sensors. Given the large number of measurement data and operating points, it is currently impossible to test all possible combinations in a reasonable time.The second solution therefore relies on business knowledge and / or recording constraints in order to choose the operating points, and therefore the measurement data and sensors, used to manage the health of the aircraft engine. However, this second solution is not optimal, in particular because it does not allow the observability of the aircraft engine to be optimized.

[0008] In an attempt to address these issues, methods for monitoring the health of an aircraft engine using an aircraft engine simulator have been developed. For example, Stenfeld, M. (2023). On model based aero engine diagnostics (PhD Thesis). Mâlardalens universitet and Wen, Y., Rahman, MF, & Xu, H. (2022). Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Measurement, 187, 110276, are two examples of such methods. However, these methods have the disadvantage of depending on the observability of the system, and do not address the issue of operating point selection.

[0009] There is therefore a need to provide a method for monitoring the health of an aircraft engine which limits, at least partially, the problems associated with the methods of the prior art. Summary of the invention

[0010] The invention provides a solution to the problems mentioned above, by making it possible to manage the health of an aircraft engine from a limited number of sensors and operational points while maximizing the observability of the aircraft engine.

[0011] One aspect of the invention relates to a method for monitoring a health x of an aircraft engine by a model inversion method, the method using a monitoring device comprising a set of sensors adapted to carry out measurements of an operation of the aircraft engine and being configured to comprise a simulator $ of the aircraft engine taking as input parameters of the health x of the aircraft engine and an operating point of the aircraft engine, and providing as output corresponding measurement data Y, the method comprising operations of: • Acquisition of a set of operational points, • Determination of a reduced set of operational points to a reduced non-observable space, the determination comprising: • Define a representation H of all operational points, • Define a quantification of the information provided by the whole operational points, • Determine a solution by characterizing an observability of the aircraft engine relative to all operational points, • Select the solution and associated operational points minimizing non-observability of the aircraft engine, and • Aircraft engine health diagnosis using model inversion method.

[0012] By means of the invention, it is possible to determine the sensor feedback data and the operational points to be used to manage the health of an aircraft engine in order to maximize the observability of the engine. Thus, the monitoring of the health of the engine is reliable and economical in resources.

[0013] 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 simulator $ is a linearized simulator $ of the aircraft engine, • determining the reduced set of operating points to a reduced non-observable space comprises determining a set of operating points V of the aircraft engine in which each operating point v of the set of operating points V is associated with at least one of the following: least one measurement data y of the measurement data set Y, the definition of the representation H comprises a calculation of a representation matrix H by performing the matrix product between the inverse of an invertible noise matrix C and a Jacobian of the simulator $, the noise matrix C containing standard deviations of noises of each operating point v of the set of operating points V, the definition of the information I comprises a calculation of an information matrix I comprising the set of operating points V and quantifying the observability of the aircraft engine allowed by the set of operating points V, the information matrix I being obtained by the matrix product between a transpose of the representation matrix HT and the representation matrix H, the solution is a sub-matrix of the information matrix I, and having a size defined by a number n of operating points considered, a dimension c of data Y and a dimension of a health vector x, and being of rank r with the rank r less than or equal to a rank of the information matrix I and the rank r being the smallest rank making it possible to obtain the observability of the aircraft engine greater than a predetermined observability threshold, the selection of the associated operational associated points comprises an identification of the operating points v present in the solution matrix, the method further comprises: • an aircraft engine health monitoring operation x using the identified operating points v and the measurement data associated with said identified operating points v, and / or • an operation for modifying the conditions of use of the aircraft engine comprising, when the health x of the aircraft engine is lower than a predetermined health level x, at least one action from among: • Perform maintenance on the aircraft engine, and / or • Change the operating conditions of the aircraft engine so that its health deteriorates less quickly, the number of operating points n final selected is a predetermined value, the determined rank r of the solution matrix corresponds to the smallest rank r allowing to obtain the observability of the aircraft engine greater than a predetermined observability threshold, and aircraft engine health monitoring includes an operation of engine health diagnosis performed using an inverse problem solving method such as Kalman filtering and / or neural network and / or thermodynamic analysis.

[0014] Another aspect of the invention relates to a system comprising a set of sensors adapted to carry out measurements on an aircraft engine and a computer configured to implement the method according to the invention.

[0015] Yet another aspect of the invention relates to an aircraft comprising the system according to the invention.

[0016] A complementary 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.

[0017] A final aspect of the invention relates to a computer-readable recording medium comprising instructions which, when executed by a computer, cause the latter to implement the steps of the method according to the invention.

[0018] The invention and its various applications will be better understood by reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0019] The figures are presented for information purposes only and in no way limit the invention: • [Fig.l] shows a block diagram illustrating the steps of an example of the method for monitoring the health of an aircraft engine according to the invention. DETAILED DESCRIPTION

[0020] Unless otherwise specified, the same element appearing in different figures has a single reference.

[0021] [Fig. 1] is a block diagram illustrating the steps of an example of method 1 according to the invention. The mandatory steps of the example of method 1 are indicated by a solid rectangle and the optional steps are indicated by a dotted rectangle.

[0022] The method 1 is implemented by a monitoring device comprising a set of sensors adapted to carry out measurements of an operation of the aircraft engine. In addition, the monitoring device is configured to comprise a simulator of the aircraft engine. For example, the device comprises a computer or a processor configured to provide a simulator of the aircraft engine. Thus, steps of the method 1 can be implemented by a processor included in a system for monitoring the health of an aircraft engine. The monitoring system preferably has the structure of a computer, in this case an on-board computer, and / or a computer. It comprises an electronic circuit in one or more parts equipped with at least one non-volatile memory, and a processor for executing logical operations. It may also include one or more other memories, of the RAM type or another type, and one or more other processors.

[0023] By "computer-implemented" is meant that the steps, or substantially all of the steps of the method 1, 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 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.

[0024] 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), a computer program comprising instructions for implementing the method being stored in the memory. The memory may also store a database. Memory is any hardware adapted for such storage, possibly comprising several distinct physical parts.

[0025] Method 1 is a method of monitoring a health x of an aircraft engine using a model inversion method.

[0026] Method 1 is based on a simulator $ of the aircraft engine. Simulator $ takes as input parameters of the health x of the aircraft engine and an operating point v of the aircraft engine and provides as output corresponding measurement data y. Simulator $ may for example be previously stored on the computer implementing method 1. In addition, a Jacobian of simulator $ is used in operation 221. This Jacobian of simulator $ may also be previously stored on the computer implementing method 1 or obtained using calculations similar to those of operation 140.

[0027] Alternatively, it is possible to use other types of simulators such as those distributed: • in the TURBO® libraries distributed by EcosimPro® and in the commercial PROOSIS® software, or • in the GasTurb® software, or • in the commercial NPSS® software, or • by Elettronicavenata Veneta®, for example the STG / EV model which is a turbine emulator.

[0028] A first operation 10 of the method 1 comprises the acquisition 10 of a set of operational points. The term “operation” in the present application is equivalent to the term “step”. The acquisition of the set of operational points may comprise obtaining a set of measurement data Y. This set of measurement data Y may come from the measurement of an operation of the aircraft engine. This set of measurement data Y may be provided by at least one sensor, having carried out the measurements during the operation of the aircraft engine. The set of measurement data Y may come from the measurement of several days or several weeks of use of the aircraft engine. The set of measurement data may also come from a single complete flight of the aircraft.

[0029] A second operation 20 of the method 1 comprises the determination of a reduced set of operational points to a reduced non-observable space.

[0030] The second operation 20 of the method 1 may comprise a first optional sub-operation 21 of determining a set of operating points V of the aircraft engine. This set of operating points V is for example a subset of a set of operating points U that can be determined from the set of measurement data. Thus, each subset of operating points V belongs to the set of operating points U. Each operating point v of the set of operating points V may be associated with at least one measurement data y of the set of measurement data Y. A first operating point may for example correspond to one or more measurement data y of the temperature of a turbine of the aircraft engine, originating from a first sensor, during the takeoff phase of the aircraft.A second operating point may for example correspond to this or these measurement data y of the temperature of the turbine of the aircraft engine, from the same first sensor, during the cruise phase of the aircraft. A third operating point may for example correspond to one or more measurement data y of the pressure of the turbine of the aircraft engine, from a second sensor, during the takeoff phase of the aircraft. In one example, the determination of the set of operating points V allows complete observability of the aircraft engine, that is to say allowing no unobservable zone of the aircraft engine to be left. In another example, the determination of the set of operating points allows observability of the aircraft engine greater than a predefined threshold.

[0031] The second operation 20 of the method 1 may also optionally comprise obtaining a simulator $ of the aircraft engine. The aircraft engine may be modeled by a system S as described below:

[0032] y = SU «)

[0033] With: • y E R6, the observation of the system, i.e. the measurement data y from at least one sensor, • x G the health of the system, so for example the modular efficiencies and flow rates of the aircraft engine, or for example the differences to a reference of modular efficiencies and flow rates of the aircraft engine, • u G Rm, an operating point.

[0034] The aircraft engine simulator $ is therefore governed by the following equation:

[0035] y^(x^

[0036] The aircraft engine simulator $ therefore takes as input the health x of the aircraft engine, or parameters of the health x of the aircraft engine, and an operating point u of the aircraft engine. The aircraft engine simulator $ thus provides as output corresponding measurement data y, i.e. measurement data y corresponding to the health x of the aircraft engine and the operating point u of the aircraft engine provided as input.

[0037] Furthermore, when measurement data have been obtained for more than one operating point v, the measurement data y can be considered as a concatenation of the sensor outputs in the operating points. Mathematically, for each subset of operating points V, with VcU, the simulator notation can be in the following form:

[0038] y) _ pS(xM)l

[0039] The second operation 20 of the method 1 may also optionally comprise the linearization of the simulator $ of the aircraft engine. The linearization of the simulator $ may be carried out by calculating the Jacobian of the simulator with a predetermined discretization step 7 and for a linearization point x0. The discretization step 7 may be between -4 and -6. The linearization point x0 corresponds to the quantities to be estimated (efficiency and flow values ​​for example). For example, this point x0 may be the output of an algorithm such as a thermodynamic analysis or a Bayesian inference from a measurement of a fixed operating point.

[0040] The second operation 20 of the method 1 comprises a second sub-operation 22 of defining a representation H of the set of operational points, for example a representation matrix H of the set of operational points.

[0041] The second sub-operation 22 of the method 1 may comprise the calculation 221 of a representation matrix H. The matrix may be calculated by performing the matrix product between the inverse of an invertible noise matrix C and the Jacobian of the simulator. The noise matrix C may contain standard deviations of noise from the measurements to

[0042]

[0043]

[0044]

[0045] each operating point v of the set of operating points V. In one example, compatible with the previous examples, a representation matrix H is calculated for each subset of operating points V included in the set of operating points U. The calculation of the representation matrix can be carried out for example using the following calculation: With • Cy, the inverse of the noise matrix, and * V 5' ( Xq, V ) ' jacc,bienne of the simulator $. The noise matrix Cv contains the standard deviations of the noise of each sensor for each operating point v of the set of operating points V. Moreover, the representation matrix is ​​the concatenation on the columns of the representation matrices of all operating points w CU, i.e. aatat T. Note that each matrix fjs contains c rows, “ Hx^ , . . •, H^un a corresponding to the number of sensors, and d columns, corresponding to the number of unknown parameters. Thus, the matrix is ​​therefore formed of N xc rows and d columns in total, with N corresponding to the number of operating points in the set of operating points U.

[0046] The second operation 20 of the method 1 comprises a third sub-operation 23 of defining a quantification of the information provided by the set of operational points, for example an information matrix I quantifying the information provided by the set of operational points

[0047] The third sub-operation 23 may comprise the calculation of an information matrix I. The information matrix I may comprise the set of operating points V. In addition, the information matrix I may quantify the observability of the aircraft engine permitted by the set of operating points V. The information matrix I may be obtained by performing the matrix product between the transpose of the representation matrix HT and the representation matrix H. Thus, the information matrix I may be obtained using the following calculation: 100481

[0049] In order to determine the observability of the system according to the selected operating points, method 1 can use the rank of the information matrix I: the higher this rank, the more observable the system is. Furthermore, it is possible to note that the rank of the information matrix I is the same as that of the representation matrix H since rang^ATA^ = ran^A^- Thus, at this stage of process 1, it is possible to consider that it now remains to be resolved:

[0050] G argmax rank (I^v) ' VcU, |V|=n ' '

[0051] With:

[0052] V^)pt, a subset of operating points v maximizing the observability of the aircraft engine.

[0053] Alternatively, due to numerical errors, it is possible to consider that it now remains to be solved:

[0054] G argmax £ (À (I^v) > e)

[0055] With: * zy ) ' 'cs eigenvalues ​​of the information matrix I, and • e, a positive scalar used as a threshold to determine whether a number is close of zero to avoid numerical errors; for example, we can define it as € = le - 6.

[0056] The second operation 20 of the method 1 comprises a fourth sub-operation 24 of determining a solution by characterizing an observability of the aircraft engine relative to the set of operational points, for example by determining a solution matrix of the information matrix I. The solution matrix characterizes an observability of the aircraft engine relative to the set of operational points and can be obtained by a method of relaxing a rank of the information matrix I.

[0057] In one example, consistent with the previous examples, the solution matrix may be obtained by searching for a sub-matrix of the information matrix I. The solution matrix has the rank r lower than the information matrix I and the rank r is the smallest rank making it possible to obtain the observability of the aircraft engine greater than a predetermined observability threshold.

[0058] It is possible to note that the rank maximization of a sub-matrix is ​​an NP-hard problem. Thus, this operation is expensive in terms of computational resources. It is therefore possible to use relaxations to a rank function that is easier to optimize in practice. Among these relaxations, it is for example possible to use: • matrix norms such as the Frobenius norm (trace), the infinite norm (maximum eigenvalue) or the nuclear norm (norm 11 of eigenvalues), • a numerical approximation including a decomposition into eigenvectors of the matrix then the determination of a threshold on the eigenvalues ​​finally the rank (or the dimension of the observable space) is approximated as the number of eigenvalues ​​larger than the set threshold.

[0059] In a first implementation mode, compatible with the previous examples, the size of the solution matrix of I is a predetermined value. In this first implementation mode, the problem can be seen as a problem of searching for a solution matrix of size n of largest volume. In a naive approach, this search for a subset of size n among N, with N corresponding to the numbers of operating points, is a search of factorial complexity.

[0060] In an example of this first mode of implementation, compatible with the previous examples, a resolution by dynamic programming, jointly with the numerical approximation of rank can be implemented. Thus, given a set of operating points V c U and the corresponding matrix rS , the observable space Obs^ is defined as corresponding to the linear space generated by all the eigenvectors. The eigenvectors are themselves defined as corresponding to the eigenvalues ​​which exceed a threshold e. Thus, mathematically, it is possible to define:

[0061] Obs^ = span(s^^! \ {o} | 32> e: s = 2s)-

[0062] In this formula: • span( ), the linear space generated by a set of vectors, • s , an eigenvector of the matrix rS , • A, an eigenvalue of the matrix A,

[0063] The concept of coverage, denoted Cv below, corresponds to the observable space given by using the measurements at the function points in an arbitrary set VL. The concept of coverage Cv can therefore be defined as follows:

[0064] Cv=dim(OZzyy) for each | V| = 1, and

[0065] = Cv + dim ( Obsu ) + d(Obsv, Obs^ For each |V| > 1

[0066] With: • ® , the operation that adds an operating point u to each sub- set V of U; for example, with a VA (' V2, ..., Vr), therefore,

[0067] V @ u- u)

[0068] Furthermore, for any arbitrary n, it is possible to define:

[0069] CV'- = max (Cv). v^u, |v|=« ' * '

[0070] Thus, by this definition, it is possible to define:

[0071] Cy»p,= (dïm(Obsu} + d{Obsvfy ueUW^pt '

[0072] In order to maximize coverage, it is possible to start the resolution with subsets of operating points of size 1, then add vectors one by one to minimize the coverage of the unobservable space. This progressive addition is possible thanks to the previous equations. Uk can therefore be defined as the set of all ordered subsets of U of size k.

[0073] Starting from the equation: = CK4 + max (dim ( Obsu ) + d(Obs^ Obs^ a pro-lt algorithm U^UW^p! dynamic programming can be applied. A first operation of this algorithm can consist in recovering the covers C fwj for each operating point u G U. This operation can be implemented using the equation: Cv = dim ( Obsv ) for each | V | = 1. A second operation of this algorithm can consist in, for m=l,2,3,...,n, selecting the set of operating points V verifying V” = argmaxCF

[0074] In order to carry out this second step, the stopping condition can be:

[0075] dim(^) = d

[0076] With: • d, the number of unknown parameters

[0077] In other words, when dim(V™ ) = d, the second operation is completed and it is possible to return the set of operating points. Conversely, when dim(y™ ) < d, it is possible to perform the same calculations as for the equation Cv =dim( Obsv) for each |V| = 1 to obtain the coverages for each u GU \ V^pt, then to calculate the coverage for each ue U \ Vopt.

[0078] Thus, at the end of the dynamic programming algorithm, an ordered set of operating points V^pt minimizing the unobservable space, or even reducing it to the null set, is obtained. This dynamic programming algorithm has a complexity equal to: n ■ N. This complexity is therefore much lower than the factorial complexity of a naive approach and allows an implementation limiting the need for computing resources. In addition, it is possible to note that the "max" operation of the second operation of the algorithm might not have a unique solution. In this case, it is possible to choose one of the solutions randomly, or to consider other particular constraints, such as for example the frequency of recording the operating point.

[0079] Alternatively, in this first mode of implementation, a greedy algorithm could be used for the numerical resolution of the problem posed.

[0080] In a second mode of implementation, compatible with the previous examples, the rank r of the solution matrix corresponds to the smallest rank allowing the observability of the aircraft engine to be obtained greater than a predetermined observability threshold. The predetermined observability threshold can for example be between 90% and 1%. In this second implementation mode, the problem therefore consists of identifying the smallest number of operating points allowing the observability to be obtained greater than the predetermined observability threshold. The problem can therefore be reformulated as follows: [°0811 Vopt = {ut GU if M* = 1)

[0082] With: • Mun vector of size N, containing a binary value, the first binary value when an operating point u is selected, and the second binary value when the operating point u is not selected otherwise.

[0083] In this second mode of implementation, the objective is therefore to obtain a vector M* maximizing the number of second binary values, or equivalently minimizing the number of selected operating points, and maximizing the quantity of information contained in the set of operating points Vopt, or equivalently maximizing the independence of the lines of Vopt.

[0084] The adjoint representation matrix H* associated with Vopt can therefore be obtained in the following manner:

[0085] H * (M*) = (Dia^) 0 Idc) H • With : • Diag, the diagonal of the matrix • Idc, the identity matrix of size c

[0086] The associated adjoint information matrix I* is therefore equal to:

[0087] f (m" Diag{M^ 0 Idc) H

[0088] With:

[0089] 0, the Kronecker product,

[0090] In order to maximize the information contained in the adjoint representation matrix H*, it is possible to minimize the rank of the residual matrix, that is, the rank of the representation matrix H minus its projection onto the adjoint representation matrix H*. The projection operator onto the adjoint representation matrix H* is given by the adjoint information matrix !*• This can then be formulated mathematically as follows:

[0091] M* = argmm \H-Hr\M}\ M 1 Z

[0092] With • , the chosen matrix norm and • a sparsity penalty parameterized by a hyper-parameter A by example linked to a predetermined parsimony rate.

[0093] In order to solve this problem, many standard optimization algorithms can be used such as primal dual algorithms or a gradient descent algorithm or a simplex type algorithm.

[0094] Alternatively, in this second implementation mode, a compressed acquisition method, in English “compressed sensing”, could be used for the numerical resolution of the problem posed.

[0095] The second operation 20 of the method 1 comprises a fifth sub-operation 25 of selecting the solution, for example the solution matrix, and the associated operational points minimizing non-observability of the aircraft engine.

[0096] This fifth sub-operation 25 of the method 1 may comprise the identification of the operating points v present in the solution matrix. These operating points correspond for example to the set of operating points Vopt of the first and second implementation modes.

[0097] A third operation 30 of the method 1 comprises the health diagnosis of the model aircraft engine in order for example to carry out the health monitoring of the aircraft engine. The health diagnosis of the aircraft engine is carried out using the model inversion method, otherwise called inverse problem solving method. Examples of methods compatible with the invention are for example Kalman filtering and / or a neural network and / or a thermodynamic analysis.

[0098] A fourth optional operation 40 of the method 1 comprises monitoring the health x of the aircraft engine using the identified operating points v and measurement data associated with said identified operating points v. In other words, monitoring the health x of the aircraft engine can be performed by collecting only the operating points v identified in operation 251.

[0099] A fifth optional operation 50 of the method 1 comprises a modification of the operating conditions of the aircraft engine. This operation 50 can be implemented when the health x of the aircraft engine is lower than a predetermined health level x. This operation 50 can for example comprise one of the following actions: • performing a maintenance operation on the aircraft engine, and / or • changing the operating conditions of the aircraft engine so that its health deteriorates less quickly.

[0100] In one example, consistent with the preceding examples, method 1 allows monitoring of a health status of multiple aircraft engines.

Claims

Claims

1. Method (1) for monitoring a health x of an aircraft engine by a model inversion method, the method using a monitoring device comprising a set of sensors adapted to carry out measurements of an operation of the aircraft engine and being configured to comprise a simulator $ of the aircraft engine taking as input parameters of the health x of the aircraft engine and an operating point of the aircraft engine, and providing as output corresponding measurement data Y, the method (1) comprising operations of: - Acquisition (10) of a set of operational points, - Determination (20) of a reduced set of operational points to a reduced non-observable space, the determination comprising: • Defining (22) a representation H of the set of operational points, • Defining (23) a quantification of the information provided by the set of operational points,• Determine (24) a solution by characterizing an observability of the aircraft engine relative to the set of operational points, • Select (25) the solution and the associated operational points minimizing a non-observability of the aircraft engine, and - Diagnosis (30) of health of the aircraft engine using the model inversion method.,

2. Method (1) according to claim 1, in which at least one of the following propositions is verified: - the simulator $ is a linearized simulator $ of the aircraft engine, - the determination (20) of the reduced set of operating points to a reduced non-observable space comprises a determination (21) of a set of operating points V of the aircraft engine in which each operating point v of the set of operating points V is associated with at least one measurement data y from the measurement data set Y, - the definition (22) of the representation H comprises a calculation (221) of a representation matrix H by performing the matrix product between the inverse of an invertible noise matrix C and a Jacobian of the simulator $, the noise matrix C containing standard deviations of noise of each operating point v of the set of operating points V, - the definition (23) of the information I comprises a calculation (231) of an information matrix I comprising the set of operating points V and quantifying the observability of the aircraft engine permitted by the set of operating points V, the information matrix I being obtained by the matrix product between a transpose of the representation matrix HT and the representation matrix H, - the solution is a sub-matrix of the information matrix I, and having a size defined by a number n of operating points considered, a dimension c of data Y and a dimension of a health vector x, and being of rank r with the rank r less than or equal to a rank of the information matrix I and the rank r being the smallest rank making it possible to obtain the observability of the aircraft engine greater than a predetermined observability threshold, and - the selection (25) of the associated operational associated points comprises an identification (251) of the operating points v present in the solution matrix.

3. Method (1) according to claim 1 or 2 further comprising: - an operation of monitoring (40) the health x of the aircraft engine using the identified operating points v and the measurement data associated with said identified operating points v, and / or - an operation of modifying (50) the conditions of use of the aircraft engine comprising, when the health x of the aircraft engine is lower than a predetermined health level x, at least one action among: • Perform maintenance on the engine aircraft, and / or • Change the operating conditions of the aircraft engine so that its health deteriorates less quickly.

4. Method (1) according to any one of claims 2 or 3 in which the number of operating points n final selected is a predetermined value.

5. Method (1) according to claim 2 in which the determined rank r of the solution matrix corresponds to the smallest rank r making it possible to obtain the observability of the aircraft engine greater than a predetermined observability threshold.

6. Method (1) according to any one of claims 2 to 5 wherein the monitoring (40) of the health x of the aircraft engine comprises an operation of diagnosing the health x of the engine carried out using a method of solving an inverse problem such as Kalman filtering and / or a neural network and / or a thermodynamic analysis.

7. System comprising a set of sensors adapted to carry out measurements on an aircraft engine and a computer configured to implement the method (1) according to one of the preceding claims.

8.

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

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

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