AERO-ENGINE FULL FLIGHT ENVELOPE MODEL ADAPTIVE MODIFICATION METHOD BASED ON DEEP LEARNING ALGORITHM

Abstract

An aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm. A dynamic parallel compensator based on a recursive neural network is adopted to compensate the error of the original nonlinear model within the full flight envelope under the condition without aero-engine performance deterioration. A modifier based on a genetic algorithm is also adopted to conduct adaptive adjustment on correction coefficients of health parameters to be modified in the original nonlinear component-level model. The health parameters to be modified are determined by a multi-attribute decision algorithm based on integrated evaluation. The sum of the modified nonlinear component-level model output and the compensator output is consistent with the aero-engine operation test output data. This provides powerful support for the design of aero-engine control systems and fault diagnosis systems.

Claims

1. An aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm, comprising the following steps: S1. generating training data and test data for establishing a dynamic parallel compensator based on a recursive neural network algorithm according to the collected test data of the condition without performance deterioration in the aero-engine full flight envelope operation test data; S2. building a dynamic parallel compensator based on a recursive neural network algorithm by using the generated training data and test data of the dynamic parallel compensator; S3. determining health parameters to be modified in the aero-engine original nonlinear component-level model by a multi-attribute decision algorithm based on integrated evaluation according to the test data of the deterioration condition of the aero-engine full flight envelope; S4. building a modifier based on a genetic algorithm, and setting the number of modifications to 20Q>0; S5. conducting adaptive modification on the correction coefficients of the health parameters to be modified in the original nonlinear component-level model; S6. calculating the sum of the modified nonlinear component-level model output and the dynamic parallel compensator output under a given input signal, and then subtracting the corresponding output data in the aero-engine full flight envelope operation test data under the given condition; if the difference e is not greater than the error threshold , 0.05>0 or the number of modifications Q is reached, entering S7; otherwise, returning to S5; S7. saving the modified correction coefficients of the health parameters to be modified.

2. The aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm according to claim 1, wherein the steps of generating the training data and test data of the dynamic parallel compensator are as follows: S1.1 assuming N of M batches of collected aero-engine full flight envelope operation test data are the test data of the condition without performance deterioration, and each batch of test data contains P samples, wherein M, N and P are natural numbers, and M>N; and in each sample, the input variables are sampling time T.sub.s, flight altitude H, Mach number Ma and fuel flow W.sub.f, and the output variables are compressor delivery pressure P.sub.3, low pressure turbine exit temperature T.sub.5, low pressure rotor speed N.sub.1 and high pressure rotor speed N.sub.2; S1.2 producing original nonlinear component-level model output: successively inputting the input variables t.sub.i, H.sub.i, Ma.sub.i and W.sub.fi in the N batches of collected test data of the condition without performance deterioration as the input signals into the aero-engine original component-level model, thus obtaining N batches of original nonlinear component-level model output: compressor delivery pressure P.sub.3i, low pressure turbine exit temperature T.sub.5i, low pressure rotor speed N.sub.1i and high pressure rotor speed N.sub.2i, wherein i=1, 2, . . . , N; S1.3 producing output data samples: respectively subtracting the test output data of the condition without performance deterioration and the original nonlinear component-level model output, thus obtaining N batches of output data samples, i.e., P.sub.3i=P.sub.3iP.sub.3i, T.sub.5i=T.sub.5iT.sub.5i, N.sub.1i=N.sub.1iN.sub.1i and N.sub.2i=N.sub.2iN.sub.2i, wherein i=1, 2, . . . , N; S1.4 conducting normalization processing: successively conducting normalization processing on W.sub.fi, P.sub.3i, T.sub.5i, N.sub.1i and N.sub.2i respectively, wherein W*.sub.fi=W.sub.fi/[Max(W.sub.fi)Min(W.sub.fi)], i=1 . . . N, W*.sub.fi is the i.sup.th batch of normalized fuel flow, Max() indicates maximization, and Min() indicates minimization; and conducting the same normalization processing on N batches of output data samples P.sub.3i, T.sub.5i, N.sub.1i and N.sub.2i, thus respectively obtaining the i.sup.th batch of normalized output data samples, including compressor delivery pressure P*.sub.3i, low pressure turbine exit temperature T*.sub.5i, low pressure rotor speed N*.sub.1i and high pressure rotor speed N*.sub.2i; S1.5 coding data: assuming N batches of test data of the condition without performance deterioration correspond to l flight altitudes and k Mach numbers, and coding the flight altitudes and the Mach numbers, i.e., establishing an lk-bit binary number, wherein each bit corresponds to the combination of one flight altitude and one Mach number; if one bit of the binary number is 1, the aero-engine operates at the flight altitude and the Mach number; S1.6 producing data samples: adding the binary number of the coded flight altitudes and Mach numbers corresponding to the i.sup.th batch of test data of the condition without performance deterioration to W*.sub.fi, P*.sub.3i, T*.sub.5i, N*.sub.1i and N*.sub.2i bit by bit, wherein the data length becomes P+lk, and i=1, 2, . . . , N; S1.7 randomly selecting four fifths of N(P+lk) samples as training samples and one fifth as test samples.

3. The aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm according to claim 2, wherein the steps of building a dynamic parallel compensator based on a recursive neural network algorithm are as follows: S2.1 establishing a recursive neural network, wherein the network parameters are: 1 input layer, 1 output layer, 10 RNN recursive neural layers, 6 linear layers and 5 activation layers, the ReLu function is selected as the activation function, the update rule is stochastic gradient descent, the momentum is 0.9, the number of iterations is 20000, the learning rate is set to 10.sup.3>l.sub.r>10.sup.5, the weight decay coefficient is set to 0.3>>10.sup.5, and the loss function R adopts the following form: $R=\frac{1}{N\left(P+lk\right)}.Math.{\left(y_t-y_n\right)}^T.Math.\left(y_t-y_n\right)+\frac{1}{2}.Math..Math..Math.w^T.Math.w$ where, y.sub.t indicates the output data in the test samples, y.sub.n indicates the output data of the parallel compensator, w indicates the weight in the recursive neural network, and the training samples are adopted for training the recursive neural network; S2.2 testing the trained recursive neural network with the test samples, and calculating the loss function; S2.3 if the value of the loss function of the test samples is greater than the index , and 0.03>0, returning to S2.1, changing the learning rate l.sub.r and the weight decay coefficient , and retraining the network; otherwise, saving the network parameters, thus completing the building of the dynamic parallel compensator based on a recursive neural network algorithm.

4. The aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm according to claim 3, wherein the steps of a multi-attribute decision algorithm based on integrated evaluation are as follows: the aero-engine health parameters comprise fan mass flow factor Q.sub.f, fan efficient factor E.sub.f, compressor flow factor Q.sub.c, compressor efficient factor E.sub.c, high pressure turbine mass flow factor Q.sub.th, high pressure turbine efficient factor E.sub.th, low pressure turbine mass flow factor Q.sub.tl, low pressure turbine efficient factor E.sub.tl, burner total pressure recovery coefficient SigComb and outer bypass total pressure recovery coefficient SigBypass; the correction coefficients of the health parameters and the allowed modification range thereof are respectively F.sub.F and [F.sub.imin, F.sub.imax], wherein i=1, . . . , 10; S3.1 in the original nonlinear component-level model, letting H=0 and Ma=0, respectively giving the fuel flow from ground idling to maximum condition according to the full flight envelope test data, setting all the correction coefficients of the health parameters to 1, and calculating the data P.sub.3s, T.sub.5s, N.sub.1s and N.sub.2s of each steady state point of the original nonlinear model by simulation; S3.2 in the original nonlinear component-level model, letting H=0 and Ma=0, respectively giving the fuel flow from ground idling to maximum condition according to the full flight envelope test data, successively increasing the correction coefficients of the health parameters from F.sub.imin to F.sub.imax by a step size of 0.05, keeping the modification values of the remaining health parameters at 1, and calculating the perturbation data P.sub.3sij, T.sub.5sij, N.sub.1sij and N.sub.2sij of each steady state point of the original nonlinear model by simulation, wherein i=1, . . . , 10 and j=1, . . . , [F.sub.imaxF.sub.imin)/0.05]; S3.3 calculating the relative deviations DP.sub.3sij=|P.sub.3sijP.sub.3s|/P.sub.3s, DT.sub.5sij=|T.sub.5sijT.sub.5s|/T.sub.5s, DN.sub.1sij=|N.sub.1sijN.sub.1s and DN.sub.2sij=|N.sub.2sijN.sub.2s|/N.sub.2s of errors of the steady state points, wherein i=1, . . . , 10 and j=1, . . . , [(F.sub.imaxF.sub.imin)/0.05]; S3.4 building the decision matrices U=[U.sub.in] and U.sub.in=[u.sub.lin,u.sub.uin] with intervals, wherein $u_{\mathrm{li}.Math..Math.1}=\underset{j}{\mathrm{Min}}\left({\mathrm{DP}}_{3.Math..Math.\mathrm{sij}}\right),u_{\mathrm{ui}.Math..Math.1}=\underset{j}{\mathrm{Max}}\left({\mathrm{DP}}_{3.Math..Math.\mathrm{sij}}\right),u_{\mathrm{li}.Math..Math.2}=\underset{j}{\mathrm{Min}}\left({\mathrm{DT}}_{5.Math..Math.\mathrm{sij}}\right),.Math.u_{\mathrm{ui}.Math..Math.2}=\underset{j}{\mathrm{Max}}\left({\mathrm{DT}}_{5.Math..Math.\mathrm{sij}}\right),u_{\mathrm{li}.Math..Math.3}=\underset{j}{\mathrm{Min}}\left({\mathrm{DN}}_{1.Math..Math.\mathrm{sij}}\right),u_{\mathrm{ui}.Math..Math.3}=\underset{j}{\mathrm{Max}}\left({\mathrm{DN}}_{1.Math..Math.\mathrm{sij}}\right),.Math.u_{\mathrm{li}.Math..Math.4}=\underset{j}{\mathrm{Min}}\left({\mathrm{DN}}_{2.Math..Math.\mathrm{sij}}\right),u_{\mathrm{ui}.Math..Math.4}=\underset{j}{\mathrm{Max}}\left({\mathrm{DN}}_{2.Math..Math.\mathrm{sij}}\right),$ i=1, . . . , 10 and n=1, . . . , 4; S3.5 calculating $B_{i.Math..Math.n}=\left[b_{i.Math..Math.n}\right]=\frac{k\left(U_{i.Math..Math.n}\right)}{\underset{i=1}{\overset{10}{.Math.}}.Math.k\left(U_{i.Math..Math.n}\right)},k\left(U_{i.Math..Math.n}\right)=\left(u_{\mathrm{lin}}+u_{\mathrm{uin}}\right)/2$ $E_{i.Math..Math.n}=\left[e_{i.Math..Math.n}\right]=\frac{1-L\left(U_{i.Math..Math.n}\right)}{10-\underset{i=1}{\overset{10}{.Math.}}.Math.L\left(U_{i.Math..Math.n}\right)},L\left(U_{i.Math..Math.n}\right)=u_{\mathrm{uin}}-u_{\mathrm{lin}}$ $q_n=\left(-\frac{1}{\ln .Math..Math.10}.Math.\underset{i=1}{\overset{10}{.Math.}}.Math.b_{i.Math..Math.n}.Math.\ln .Math..Math.b_{i.Math..Math.n}\right)+\left(1-\right).Math.\left(-\frac{1}{\ln .Math..Math.10}.Math.\underset{i=1}{\overset{10}{.Math.}}.Math.e_{i.Math..Math.n}.Math.\ln .Math..Math.e_{i.Math..Math.n}\right)$ where, B.sub.in is a midpoint normalization matrix, E.sub.in is a length normalization matrix, q.sub.n is the information entropy of the n.sup.th attribute, 0<<1 is the balance factor, i=1, . . . , 10 and n=1, . . . , 4; calculating the entropy weight $w_n=\frac{1-q_n}{\underset{n=1}{\overset{4}{.Math.}}.Math.\left(1-q_n\right)};$ S3.6 calculating the entropy weight decision value
v.sub.i=1.sub.n=1.sup.4w.sub.n(|u.sub.linu*.sub.in|+|u.sub.uinu*.sub.in|)/2 where, u*.sub.in=(u.sub.lin+u.sub.uin)/2, i=1, . . . , 10 and n=1, . . . , 4; S3.7 constructing a weighted standardization decision matrix J.sub.in=U.sub.inw.sub.n, and determining the sizes of a positive ideal solution {tilde over (c)}.sup.+ and a negative ideal solution {tilde over (c)}.sup. respectively as $\{\begin{array}{c}{\tilde{c}}^{+}=\left({\tilde{c}}_1^{+},\mathrm{.Math.}.Math.,{\tilde{c}}_4^{+}\right)\\ {\tilde{c}}^{-}=\left({\tilde{c}}_1^{-},\mathrm{.Math.}.Math.,{\tilde{c}}_4^{-}\right)\end{array}.Math..Math.\mathrm{where},{\tilde{c}}_n^{+}=\underset{i}{\mathrm{Max}}\left(J_{i.Math..Math.n}\right).Math..Math.\mathrm{and}.Math..Math.{\tilde{c}}_n^{-}=\underset{i}{\mathrm{Min}}\left(J_{i.Math..Math.n}\right);$ calculating the distance $\{\begin{array}{c}{d}_i^{+}=\sqrt{\underset{n=1}{\overset{4}{.Math.}}.Math.{\left(J_{i.Math..Math.n}-{\tilde{c}}_n^{+}\right)}^2}\\ d_i^{-}=\sqrt{\underset{n=1}{\overset{4}{.Math.}}.Math.{\left(J_{i.Math..Math.n}-{\tilde{c}}_n^{-}\right)}^2}\end{array};$ where, d.sup.+.sub.i is the distance between the weighted standardization decision matrix J.sub.in and the positive ideal solution {tilde over (c)}.sup.+, and the d.sup..sub.i is the distance between the weighted standardization decision matrix J.sub.in and the negative ideal solution {tilde over (c)}.sup.; calculating the decision value $c_i=\frac{d_i^{-}}{d_i^{-}+d_i^{+}},$ wherein i=1, . . . , 10; S3.8 calculating the integrated decision value F.sub.i=(v.sub.i+c.sub.i), wherein is the amplification coefficient and is 1, and i=1, . . . , 10, sequencing the integrated decision values from large to small, and selecting the first four parameters as the health parameters to be modified.

5. The aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm according to claim 4, wherein the parameters of the modifier based on a genetic algorithm are set as follows: the population size of the genetic algorithm is 100, the number of iterations is 20, the number of good generations is 5, the probability of mutation is generated by Gaussian distribution, the probability of crossover is 0.8, the fitness function of the genetic algorithm is the sum of the aero-engine full flight envelope operation test data of P.sub.3, T.sub.5, N.sub.1 and N.sub.2 and the error of the aero-engine nonlinear component-level model output modified by the modifier, and the number of variables is 4.

Description

DESCRIPTION OF DRAWINGS

[0040] FIG. 1 is a schematic diagram of a system structure of an aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm;

[0041] FIG. 2 is a flow diagram of an algorithm of an aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm;

[0042] FIG. 3 is a flow diagram of a generating algorithm for the training data and test data of a dynamic parallel compensator;

[0043] FIG. 4 is a flow diagram of a multi-attribute decision algorithm based on integrated evaluation;

[0044] FIG. 5 is a comparison diagram of P.sub.3 data of original nonlinear component-level model output and test data under a condition;

[0045] FIG. 6 is a comparison diagram of T.sub.5 data of original nonlinear component-level model output and test data under a condition;

[0046] FIG. 7 is an effect diagram of aero-engine full flight envelope model adaptive modification P.sub.3 based on a deep learning algorithm;

[0047] FIG. 8 is an effect diagram of aero-engine full flight envelope model adaptive modification T.sub.5 based on a deep learning algorithm.

DETAILED DESCRIPTION

[0048] The present invention is further described below in combination with the drawings. The present invention replies on the background of a nonlinear mathematical model and test data of a certain type of low bypass ratio turbofan engine, and the system structure diagram is shown in FIG. 1.

[0049] As shown in FIG. 2, an aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm, comprising the following steps:

[0050] S1. generating training data and test data for establishing a dynamic parallel compensator based on a recursive neural network algorithm according to the collected test data of the condition without performance deterioration in the aero-engine full flight envelope operation test data;

[0051] S2. building a dynamic parallel compensator based on a recursive neural network algorithm by using the generated training data and test data of the dynamic parallel compensator;

[0052] S3. determining health parameters to be modified in the aero-engine original nonlinear component-level model by a multi-attribute decision algorithm based on integrated evaluation;

[0053] S4. building a modifier based on a genetic algorithm, and setting the number of modifications to 20Q>0, wherein Q is set to 10 in the embodiment;

[0054] S5. conducting adaptive modification on the correction coefficients of the health parameters to be modified in the original nonlinear component-level model;

[0055] S6. calculating the sum of the modified nonlinear component-level model output and the dynamic parallel compensator output under a given input signal, and then subtracting the corresponding output data in the aero-engine full flight envelope operation test data under the given condition; if the difference e is not greater than the error threshold =0.04 (0.05>0) or the number of modifications Q is reached, entering S7; otherwise, returning to S5;

[0056] S7. saving the modified correction coefficients of the health parameters to be modified.

[0057] As shown in FIG. 3, the steps of generating the training data and test data of the dynamic parallel compensator are as follows:

[0058] S1. assuming N=150 of M=980 batches of collected aero-engine full flight envelope operation test data are the test data of the condition without performance deterioration, and each batch of test data contains P=1000 samples. In each sample, the input variables are sampling time T.sub.s=0.025, flight altitude H, Mach number Ma and fuel flow W.sub.f, and the output variables are compressor delivery pressure P.sub.3, low pressure turbine exit temperature T.sub.5, low pressure rotor speed N.sub.1 and high pressure rotor speed N.sub.2;

[0059] S2. producing original nonlinear component-level model output: successively inputting the input variables t.sub.i, H.sub.i, Ma.sub.i and W.sub.fi in the N batches of collected test data of the condition without performance deterioration as the input signals into the aero-engine original component-level model, thus obtaining N batches of original nonlinear component-level model output (compressor delivery pressure P.sub.3i, low pressure turbine exit temperature T.sub.5i, low pressure rotor speed N.sub.1i and high pressure rotor speed N.sub.2i), wherein i=1, 2, . . . , N;

[0060] S3. producing output data samples: respectively subtracting the test output data of the condition without performance deterioration and the original model output, thus obtaining N batches of output data samples, i.e., P.sub.3i=P.sub.3iP.sub.3i, T.sub.5i=T.sub.5iT.sub.5i, N.sub.1i=N.sub.1iN.sub.1i and N.sub.2i=N.sub.2iN.sub.2i, wherein i=1, 2, . . . , N;

[0061] S4. conducting normalization processing: successively conducting normalization processing on W.sub.fi, P.sub.3i, T.sub.5i, N.sub.1i and N.sub.2i respectively, for example, W*.sub.fi=W.sub.fi/[Max(W.sub.fi)Min(W.sub.fi)], i=1 . . . N, W*.sub.fi is the i.sup.th batch of normalized fuel flow, Max() indicates maximization, and Min() indicates minimization; and conducting the same normalization processing on N batches of output data samples P.sub.3i, T.sub.5i, N.sub.1i and N.sub.2i, thus respectively obtaining the i.sup.th batch of normalized output data samples, including compressor delivery pressure P*.sub.3i, low pressure turbine exit temperature T*.sub.5i, low pressure rotor speed N*.sub.1i and high pressure rotor speed N*.sub.2i;

[0062] S5. conducting data coding: assuming N batches of test data of the condition without performance deterioration correspond to l=3 flight altitudes and k=3 Mach numbers, and coding the flight altitudes and the Mach numbers, i.e., establishing a 9-bit binary number, wherein each bit corresponds to the combination of one flight altitude and one Mach number;

[0063] S6. producing data samples: adding the binary number of the coded flight altitudes and Mach numbers corresponding to the i.sup.th batch of test data of the condition without performance deterioration to W*.sub.fi, P*.sub.3i, T*.sub.5i, N*.sub.1i and N*.sub.2i bit by bit, wherein the data length becomes 1009, and i=1, 2, . . . , N;

[0064] S7. randomly selecting 1201009 samples as training samples and 301009 samples as test samples.

[0065] The steps of building a dynamic parallel compensator based on a recursive neural network algorithm are as follows:

[0066] S1. establishing a recursive neural network, wherein the network parameters are: 1 input layer, 1 output layer, 10 RNN (recursive neural layers), 6 linear layers and 5 activation layers, the ReLu function is selected as the activation function, the update rule is stochastic gradient descent, the momentum is 0.9, the number of iterations is 20000, the learning rate is set to l.sub.r=10.sup.4, the weight decay coefficient is set to =10.sup.3, and the loss function R adopts the following form:

[00008]$R=\frac{1}{N\left(P+lk\right)}.Math.{\left(y_t-y_n\right)}^T.Math.\left(y_t-y_n\right)+\frac{1}{2}.Math..Math..Math.w^T.Math.w$

[0067] where, y.sub.t indicates the output data in the test samples, y.sub.n indicates the output data of the parallel compensator, w indicates the weight in the recursive neural network, and the training samples are adopted for training the recursive neural network;

[0068] S2. testing the trained recursive neural network with the test samples, and calculating the loss function;

[0069] S3. if the value of the loss function of the test samples is greater than the index =0.02, returning to S1, changing the learning rate l.sub.r, and the weight decay coefficient , and retraining the network; otherwise, saving the network parameters, thus completing the building of the dynamic parallel compensator based on a recursive neural network algorithm.

[0070] As shown in FIG. 4, the steps of a multi-attribute decision algorithm based on integrated evaluation are as follows:

[0071] The aero-engine health parameters comprise fan mass flow factor Q.sub.f, fan efficient factor E.sub.f, compressor flow factor Q.sub.c, compressor efficient factor E.sub.c, high pressure turbine mass flow factor Q.sub.th, high pressure turbine efficient factor E.sub.th, low pressure turbine mass flow factor Q.sub.tl, low pressure turbine efficient factor E.sub.tl, burner total pressure recovery coefficient SigComb and outer bypass total pressure recovery coefficient SigBypass; and the correction coefficients of the health parameters and the allowed modification range thereof are respectively F.sub.i and [F.sub.imin, F.sub.imax,], wherein i=1, . . . , 10, and the requirements of the provided modification range are shown in Table 1;

TABLE-US-00001 TABLE 1 Modification Range of Correction Coefficients of Health Parameters Parameter Q.sub.f E.sub.f Q.sub.c E.sub.c Q.sub.th E.sub.th Q.sub.tl E.sub.tl SigComb SigBypass F.sub.imin 0.7 0.75 0.7 0.7 0.75 0.7 0.7 0.7 0.8 0.7 F.sub.imax 1.05 1.1 1.05 1.1 1.1 1.1 1.1 1.1 1.1 1.1

[0072] S1. in the original nonlinear component-level model, letting H=0 and Ma=0, respectively giving the fuel flow from ground idling to maximum condition according to the full flight envelope test data, setting all the correction coefficients of the health parameters to 1, and calculating the data P.sub.3s, T.sub.5s, N.sub.1s and N.sub.2s of each steady state point of the original nonlinear model by simulation;

[0073] S2. in the original nonlinear component-level model, letting H=0 and Ma=0, respectively giving the fuel flow from ground idling to maximum condition according to the full flight envelope test data, successively increasing the correction coefficients of the health parameters from F.sub.imin, to F.sub.imax by a step size of 0.05, keeping the modification values of the remaining health parameters at 1, and calculating the perturbation data P.sub.3sij, T.sub.5sij, N.sub.1sij and N.sub.2sij of each steady state point of the original nonlinear model by simulation, wherein i=1, . . . , 10 and j=1, . . . , [F.sub.imaxF.sub.imin)/0.05];

[0074] S3. calculating the relative deviations DP.sub.3sij=|P.sub.3sijP.sub.3s|/P.sub.3s, DT.sub.5sij=|T.sub.5sijT.sub.5s|/T.sub.5s, DN.sub.1sij=|N.sub.1sijN.sub.1s|/N.sub.1s and DN.sub.2sy=|N.sub.2sijN.sub.2s|/N.sub.2s of errors of the steady state points, wherein i=1, . . . , 10 and j=1, . . . , [(F.sub.imaxF.sub.imin)/0.05];

[0075] S4. building the decision matrices U=[U.sub.in] and u.sub.in=[u.sub.lin,u.sub.uin] with intervals, wherein

[00009]$u_{\mathrm{li}.Math..Math.1}=\underset{j}{\mathrm{Min}}\left({\mathrm{DP}}_{3.Math..Math.\mathrm{sij}}\right),u_{\mathrm{ui}.Math..Math.1}=\underset{j}{\mathrm{Max}}\left({\mathrm{DP}}_{3.Math..Math.\mathrm{sij}}\right),u_{\mathrm{li}.Math..Math.2}=\underset{j}{\mathrm{Min}}\left({\mathrm{DT}}_{5.Math..Math.\mathrm{sij}}\right),.Math.u_{\mathrm{ui}.Math..Math.2}=\underset{j}{\mathrm{Max}}\left({\mathrm{DT}}_{5.Math..Math.\mathrm{sij}}\right),u_{\mathrm{li}.Math..Math.3}=\underset{j}{\mathrm{Min}}\left({\mathrm{DN}}_{1.Math..Math.\mathrm{sij}}\right),u_{\mathrm{ui}.Math..Math.3}=\underset{j}{\mathrm{Max}}\left({\mathrm{DN}}_{1.Math..Math.\mathrm{sij}}\right),.Math.u_{\mathrm{li}.Math..Math.4}=\underset{j}{\mathrm{Min}}\left({\mathrm{DN}}_{2.Math..Math.\mathrm{sij}}\right),u_{\mathrm{ui}.Math..Math.4}=\underset{j}{\mathrm{Max}}\left({\mathrm{DN}}_{2.Math..Math.\mathrm{sij}}\right),$

[0076] i=1, . . . , 10 and n=1, . . . , 4;

[0077] S5. calculating

[00010]$B_{i.Math..Math.n}=\left[b_{i.Math..Math.n}\right]=\frac{k\left(U_{i.Math..Math.n}\right)}{\underset{i=1}{\overset{10}{.Math.}}.Math.k\left(U_{i.Math..Math.n}\right)},k\left(U_{i.Math..Math.n}\right)=\left(u_{\mathrm{lin}}+u_{\mathrm{uin}}\right)/2$$E_{i.Math..Math.n}=\left[e_{i.Math..Math.n}\right]=\frac{1-L\left(U_{i.Math..Math.n}\right)}{10-\underset{i=1}{\overset{10}{.Math.}}.Math.L\left(U_{i.Math..Math.n}\right)},L\left(U_{i.Math..Math.n}\right)=u_{\mathrm{uin}}-u_{\mathrm{lin}}$$q_n=\left(-\frac{1}{\ln .Math..Math.10}.Math.\underset{i=1}{\overset{10}{.Math.}}.Math.b_{i.Math..Math.n}.Math.\ln .Math..Math.b_{i.Math..Math.n}\right)+\left(1-\right).Math.\left(-\frac{1}{\ln .Math..Math.10}.Math.\underset{i=1}{\overset{10}{.Math.}}.Math.e_{i.Math..Math.n}.Math.\ln .Math..Math.e_{i.Math..Math.n}\right)$

where, the balance factor =0.5, i=1, . . . , 10 and n=1, . . . , 4;
calculating the entropy weight

[00011]$w_n=\frac{1-q_n}{\underset{n=1}{\overset{4}{.Math.}}.Math.\left(1-q_n\right)};$

[0078] S6. calculating the entropy weight decision value

v.sub.i=1.sub.n=1.sup.4w.sub.n(|u.sub.linu*.sub.in|+|u.sub.uinu*.sub.in|)/2

where, u*.sub.in=(u.sub.lin+u.sub.uin)/2, i=1, . . . , 10 and n=1, . . . , 4, it is the entropy weight decision value in the embodiment;

[0079] S7. constructing a weighted standardization decision matrix J.sub.in=U.sub.inw.sub.n, and determining the sizes of a positive ideal solution {tilde over (c)}.sup.+ and a negative ideal solution {tilde over (c)}.sup. respectively as

[00012]$\{\begin{array}{c}{\tilde{c}}^{+}=\left({\tilde{c}}_1^{+},\mathrm{.Math.}.Math.,{\tilde{c}}_4^{+}\right)\\ {\tilde{c}}^{-}=\left({\tilde{c}}_1^{-},\mathrm{.Math.}.Math.,{\tilde{c}}_4^{-}\right)\end{array}.Math..Math.\mathrm{where},{\tilde{c}}_n^{+}=\underset{i}{\mathrm{Max}}\left(J_{i.Math..Math.n}\right).Math..Math.\mathrm{and}.Math..Math.{\tilde{c}}_n^{-}=\underset{i}{\mathrm{Min}}\left(J_{i.Math..Math.n}\right);$

calculating the distance

[00013]$\{\begin{array}{c}{d}_i^{+}=\sqrt{\underset{n=1}{\overset{4}{.Math.}}.Math.{\left(J_{i.Math..Math.n}-{\tilde{c}}_n^{+}\right)}^2}\\ d_i^{-}=\sqrt{\underset{n=1}{\overset{4}{.Math.}}.Math.{\left(J_{i.Math..Math.n}-{\tilde{c}}_n^{-}\right)}^2}\end{array};$

calculating the decision value

[00014]$c_i=\frac{d_i^{-}}{d_i^{-}+d_i^{+}},$

wherein i=1, . . . , 10;

[0080] S8. calculating the integrated decision values F.sub.i=(v.sub.i+c.sub.i), wherein i=1, . . . , 10, sequencing the integrated decision values from large to small, and selecting the first four parameters as the health parameters to be modified. The parameters selected in the embodiment are E.sub.tl, Q.sub.tl, E.sub.f and Q.sub.f.

[0081] The parameters of the modifier based on a genetic algorithm are set as follows: the population size of the genetic algorithm is 100, the number of iterations is 20, the number of good generations is 5, the probability of mutation is generated by Gaussian distribution, the probability of crossover is 0.8, the fitness function of the genetic algorithm is the sum of the aero-engine full flight envelope operation test data of P.sub.3, T.sub.5, N.sub.1 and N.sub.2 and the error of the aero-engine nonlinear component-level model output modified by the modifier, and the number of variables is 4. The modification results of the correction coefficients of the health parameters are as follows:

E.sub.tl=0.8879628078986851; Q.sub.tl=0.9981193434041549;

E.sub.f=0.9557873575305739; Q.sub.f=0.9897485939854536.

[0082] The difference between the original model before modification and the test data is shown in FIG. 5 and FIG. 6 which are respectively the comparison diagrams of P.sub.3 data and T.sub.5 data of the original nonlinear component-level model output and the test data under a condition. The average relative errors of P.sub.3 and T.sub.5 of original modeling are 20.27% and 17.30%.

[0083] FIG. 7 is a comparison diagram of P.sub.3 of the model output modified by the aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm provided by the present invention and the test data, and the average relative error of modeling is 1.42%.

[0084] FIG. 8 is a comparison diagram of T.sub.5 of the model output modified by the aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm provided by the present invention and the test data, and the average relative error of modeling is 1.91%.

[0085] In conclusion, the steady state performance and the transient state performance can be obviously improved after modification by the aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm.