Autonomous fault-tolerant correction method and device for input parameters of airborne adaptive model of aero-engine, electronic equipment and storage medium

Abstract

The invention discloses an autonomous fault-tolerant correction method and device for input parameters of an airborne adaptive model of an aero-engine, electronic equipment and a storage medium. The method comprises the steps that S1, a nonlinear core engine rotating speed controller is designed; s2, fault mutual judgment of a fan rotating speed sensor and a core machine rotating speed sensor is carried out according to relative errors of the auxiliary airborne model and three parameters of the fan rotating speed, the core machine rotating speed and the high-pressure compressor outlet static pressure of a real engine; the input parameter measured value deviation calculated by the corresponding rotating speed controller is selected according to the fault mutual judgment result to be used for correcting the input parameter measured value; and S3, realizing autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model according to the corrected input parameter measurement value under the condition that the rotating speed sensor has a fault. According to the method, the safety problem caused by insufficient correction due to faults of the rotating speed sensor in an existing input parameter correction method is solved, and conditions are created for a high-precision airborne adaptive model.

Description

Technical Field

[0001] The present invention relates to the technical field of aero-engines, and in particular to a method, device, equipment and storage medium for autonomous fault-tolerant correction of input parameters of an onboard adaptive model of an aero-engine. Background Art

[0002] With the development of the new generation of intelligent control systems for aircraft engines, the establishment of an onboard adaptive model is an important technical means to achieve active control and health diagnosis of engines. However, in the past 30 years, the theoretical basis of onboard adaptive model technology has been very mature, but existing studies have assumed that the input of the onboard adaptive model is always the same as the input of the real engine. However, there is a large measurement uncertainty in the measurement of input parameters such as fuel flow in engineering, and the obtained fuel value is not accurate enough. It is difficult to ensure that the input of the onboard adaptive model is the same as the input of the real engine, and thus it is impossible to track the state of the real engine, resulting in low confidence in the estimated component health parameters and performance parameters. At present, this problem can be solved by the self-correction method of the input parameters of the onboard adaptive model of aircraft engines (Chen Qian et al., CN202411604208.4), but this method has a defect. It uses an additional auxiliary onboard model and a fan nonlinear speed controller to perform online correction on the measured values ​​of the input parameters of the onboard adaptive model of aircraft engines, and updates the auxiliary onboard model by considering the performance degradation of engine components throughout the life cycle. The nonlinear speed controller of the fan uses the input parameter measurement value as the feedforward, the fan speed measured in real time by the real engine as the reference value, and the fan speed estimated by the auxiliary airborne model as the feedback value to calculate the input parameter measurement value deviation. Therefore, the fan speed value measured in real time by the real engine must be very accurate, but it is inevitable that faults such as offset and drift may occur during the entire life cycle of the engine. Once the fan speed sensor fails, the measured fan speed is not equal to the actual speed, and the auxiliary airborne model no longer tracks the actual reference speed. At this time, the calculated input parameter measurement value deviation is not equal to the actual measurement deviation, which leads to insufficient correction of the input parameter measurement value, and finally causes the failure of the airborne adaptive model. Therefore, in order to effectively realize the engineering application of the input parameter self-correction method of the airborne adaptive model of the aircraft engine, it is necessary to carry out fault-tolerant method research on it to avoid the risk of insufficient correction of the input parameter measurement value caused by the sudden failure of the fan speed sensor. Summary of the invention

[0003] On the one hand, the present application provides an autonomous fault-tolerant correction method for the input parameters of an aircraft engine onboard adaptive model, which is used to solve the problem that the input parameter measurement values ​​of the existing adaptive model input parameter self-correction methods are insufficiently corrected, ultimately causing the onboard adaptive model to fail.

[0004] This application is implemented through the following scheme: An autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model comprises the following steps: S1. Based on the existing airborne adaptive model input parameter self-correction method, a nonlinear core engine speed controller is designed with reference to the design principle of a nonlinear fan speed controller. The nonlinear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as a reference value, uses the core engine speed estimated by the auxiliary airborne model as a feedback value, uses the error between the reference value and the feedback value as input, and uses the input parameter measurement value as a feedforward amount to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation; S2. performing mutual fault judgment of the fan speed sensor and the core speed sensor according to the relative errors of the three parameters of the auxiliary airborne model and the real engine, namely, the fan speed, the core speed and the high-pressure compressor outlet static pressure, and selecting the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core speed controller to correct the input parameter measurement value through the sensor fault signal output by the fault mutual judgment result; S3. Implement autonomous fault-tolerant correction of input parameters of the onboard adaptive model and the auxiliary onboard model in the event of a failure of the fan speed sensor or the core engine speed sensor according to the corrected input parameter measurement values.

[0005] Furthermore, it is characterized in that the step S2 specifically comprises the steps of: S21, preprocessing the relative errors between the calculation-assisted airborne model and the three parameters of the fan speed, core engine speed and high-pressure compressor outlet static pressure of the real engine; S22. Compare the relative errors of the three parameters after preprocessing with a preset threshold value, and determine whether the fan speed sensor and the core engine speed sensor are faulty based on whether the relative error crosses the threshold value, thereby giving a discrimination signal to select the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller for correcting the input parameter measurement value.

[0006] Furthermore, the step S21 specifically includes the steps of: S211. Calculate the relative error between the fan speed, core engine speed and high pressure compressor outlet static pressure of the auxiliary airborne model and the real engine:

[0007] Among them, e Nf is the relative error between the actual engine measurement and the fan speed estimated by the onboard model, e Nc is the relative error between the actual engine measurement and the core speed estimated by the onboard model, e ps30Nf is the relative error between the actual engine measurement and the high pressure compressor outlet static pressure estimated by the onboard model, m is the actual measured value of the real engine fan speed, Nf e is the fan speed estimated by the onboard model, E Nf Nc is the relative error between the actual engine measurement and the fan speed estimated by the onboard model. m is the actual measured value of the actual engine core speed, Nc e is the core engine speed estimated by the onboard model, E Nc is the relative error between the actual engine measurement and the core speed estimated by the onboard model, Ps30 m is the actual measured value of the static pressure at the outlet of the high pressure compressor of the real engine, Ps30 e is the high pressure compressor outlet static pressure estimated by the onboard model, E ps30 is the relative error between the actual engine measurement and the high pressure compressor outlet static pressure estimated by the onboard model; S212, using a first-order low-pass filter to Nf 、e Nc 、e ps30 Perform denoising and obtain the relative error e after filtering. Nf,f 、e Nc,f 、e ps30,f .

[0008] Furthermore, the step S22 specifically includes the steps of: S221, Initialization: Setting the initial relative error judgment threshold thr = 0.01, selection factor α = 1, sensor fault type sfFlag = 0, where α = 1 indicates that the input parameter measurement deviation calculated by the nonlinear fan speed controller is selected, α = 0 indicates that the input parameter measurement deviation calculated by the nonlinear core speed controller is selected, sfFlag = 0 indicates that the fan speed sensor has no fault; sfFlag = 1 indicates that the fan speed sensor has a fault; sfFlag = 2 indicates that the core speed sensor has a fault; S222, when the fan speed sensor fails, if sfFlag = 2, regardless of whether the fan speed sensor fails, the onboard adaptive model fails, and step S2212 is executed; if sfFlag ≠ 2, determine whether the fan speed sensor fails according to the current value of sfFlag, and execute step S223; S223, determine whether α is equal to 1, if so, execute step S224; if not, execute step S228; S224. Judgment e Nf,f of eNf,f Absolute value | e Nf,f |Is it less than or equal to thr If yes, set α = 1, sfFlag = 0, and execute step S225; S225, Judgment e Nc,f The absolute value of | e Nc,f | thr / 2 and e ps30 The absolute value of |e ps30 | is greater than thr? If yes, set α = 0, sfFlag = 1, and execute step S2211; if no, execute step S226; S226, Judgment | e Nc,f |Greater than thr / 2 and |e ps30 |Less than or equal to thr Are all satisfied? If so, set α = 0, sfFlag = 2, and execute step S2211; if not, set α = 1, sfFlag = 0, and execute step S2211; S227, judgement is | e Nc,f |No less than or equal to thr / 2 ; If yes, set α = 0, sfFlag = 1, and execute step S228; if no, execute step S229; S228, judgement |e ps30 |Is it less than or equal to thr If yes, then execute step S2211; if no, then execute step S2212; S229, judgement |e ps30 |Is it less than or equal to thr, If yes, set α = 0, sfFlag = 1, and execute step S2211; if no, set α = 1, sfFlag = 2, and execute step S2211; S2210, judgement | e Nc,f |Is it greater than thr / 2 , if yes, set α = 1, sfFlag = 0; if no, execute step S229; S2211, output α and sfFlag values, and set k = k+1 , propagate the values ​​of α and sfFlag to the next moment, and then execute step S222; S2212, the determination is completed, and a nonlinear fan speed controller or a nonlinear core engine speed controller is selected according to the value of the selection factor α to calculate the input parameter measurement value deviation for correcting the input parameter measurement value.

[0009] Further, in step S2212, a nonlinear fan speed controller or a nonlinear core engine speed controller is selected according to the value of the selection factor α to calculate the input parameter measurement value deviation Δu as: Δu=αΔu f +(1-α) Δu c Among them, Δu f Deviation of the measured value of the input parameter calculated for the nonlinear fan speed controller, Δu c Deviations from measured values ​​of input parameters calculated for the nonlinear fan speed controller.

[0010] Further, in step S3, after the autonomous fault-tolerant correction of the input parameters of the onboard adaptive model and the auxiliary onboard model, the input parameter measurement values ​​u of the onboard adaptive model and the auxiliary onboard model are actually given. a for: u a =u m +Δu, Among them, u m Measure the values ​​for the input parameters.

[0011] Furthermore, the step S3 further comprises the steps of: When the input parameter measurement values ​​actually given to the airborne adaptive model and the auxiliary airborne model are given to the airborne adaptive model, the sensor fault type sfFlag value and the steady-state signal output by the steady-state discrimination module are used to jointly decide whether to correct the auxiliary airborne model. If sfFlag = 0, no correction is made; if sfFlag = 1, Nf m =Nf e ; If sfFlag = 2, then let Nc m = Nc e ; The switch is closed only when the steady-state signal output by the steady-state discrimination module is steady and only one sensor fault exists, and the outlier-median average filtering algorithm is used to calculate the degradation amount of the health parameter. After filtering Update the auxiliary onboard model, otherwise disconnect the switch and do not update the auxiliary onboard model.

[0012] On the other hand, the present application also provides an autonomous fault-tolerant correction device for input parameters of an aircraft engine onboard adaptive model, comprising: A core engine speed controller establishment module is used to design a nonlinear core engine speed controller based on the existing airborne adaptive model input parameter self-correction method and with reference to the design principle of a nonlinear fan speed controller. The nonlinear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as a reference value, uses the core engine speed estimated by the auxiliary airborne model as a feedback value, uses the error between the reference value and the feedback value as input and uses the input parameter measurement value as a feedforward amount to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation; A sensor fault mutual judgment module is used to perform mutual judgment of the faults of the fan speed sensor and the core engine speed sensor according to the relative errors of the three parameters of the auxiliary airborne model and the real engine, namely, the fan speed, the core engine speed and the high-pressure compressor outlet static pressure, and select the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller to correct the input parameter measurement value through the sensor fault signal output by the fault mutual judgment result; The model autonomous fault-tolerant correction module is used to realize autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model according to the corrected input parameter measurement values ​​when a fan speed sensor or a core engine speed sensor fails.

[0013] On the other hand, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for autonomous fault-tolerant correction of input parameters of an onboard adaptive model of an aircraft engine when executing the computer program.

[0014] On the other hand, the present application further provides a storage medium, which includes a stored program, and when the program is running, controls the device where the storage medium is located to execute the steps of the autonomous fault-tolerant correction method for input parameters of an onboard adaptive model of an aircraft engine.

[0015] Compared with the prior art, this application has the following beneficial effects: The invention provides an autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model. The autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model improves the autonomous fault-tolerant capability of correcting the measured values ​​of input parameters of the onboard adaptive model by designing a dual redundant control strategy of a fan speed and a core engine speed. Based on mutual fault discrimination of speed sensor, when a fan speed sensor fails, a nonlinear fan speed controller and a nonlinear core engine speed controller are switched in time, thereby ensuring that the deviation of input parameter measured values ​​is always accurately estimated, the input parameter measured values ​​are corrected, and finally the input parameter values ​​of the onboard adaptive model are equal to those of a real engine. The safety problem caused by insufficient correction due to speed sensor failure in the existing airborne adaptive model input parameter correction method is solved, and conditions are created for a high-precision airborne adaptive model.

[0016] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application: Figure 1 It is a schematic diagram of the autonomous fault-tolerant correction process of input parameters of an aircraft engine onboard adaptive model according to a preferred embodiment of the present application; Figure 2 is a schematic flow chart of sub-steps of step S2; Figure 3 It is a schematic diagram of the principle of the autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model according to a preferred embodiment of the present application; Figure 4 It is a schematic diagram of a module of an autonomous fault-tolerant correction device for input parameters of an aircraft engine onboard adaptive model according to a preferred embodiment of the present application; Figure 5 is a schematic block diagram of an electronic device entity in a preferred embodiment of the present application; Figure 6 It is a diagram of the internal structure of a computer device of a preferred embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings, but the present application can be implemented in a variety of different ways defined and covered below.

[0019] like Figure 1 As shown, the preferred embodiment of the present application provides an autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model, comprising the steps of: S1. Based on the existing airborne adaptive model input parameter self-correction method, a nonlinear core engine speed controller is designed with reference to the design principle of a nonlinear fan speed controller. The nonlinear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as a reference value, uses the core engine speed estimated by the auxiliary airborne model as a feedback value, uses the error between the reference value and the feedback value as input, and uses the input parameter measurement value as a feedforward amount to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation; S2. performing mutual fault judgment of the fan speed sensor and the core speed sensor according to the relative errors of the three parameters of the auxiliary airborne model and the real engine, namely, the fan speed, the core speed and the high-pressure compressor outlet static pressure, and selecting the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core speed controller to correct the input parameter measurement value through the sensor fault signal output by the fault mutual judgment result; S3. Implement autonomous fault-tolerant correction of input parameters of the onboard adaptive model and the auxiliary onboard model in the event of a failure of the fan speed sensor or the core engine speed sensor according to the corrected input parameter measurement values.

[0020] Because the current method for constructing an onboard adaptive model of an aircraft engine assumes that the sensor measurement has not failed, once a measurement failure occurs, the correction of the onboard adaptive model will deviate from the state of the real engine or even fail. That is, since the self-correction method of the input parameter of the onboard adaptive model mainly compensates for the lack of input parameter measurement accuracy by controlling the fan speed to match the real engine speed, it is very dependent on the measurement confidence of the fan speed sensor for the fan speed. Therefore, this embodiment provides an autonomous fault-tolerant correction method for the input parameter of an onboard adaptive model of an aircraft engine. The autonomous fault-tolerant correction method for the input parameter of an onboard adaptive model of an aircraft engine improves the autonomous fault-tolerant capability of the correction of the input parameter measurement value of the onboard adaptive model by designing a dual redundant control strategy for the fan speed and the core engine speed. Based on the mutual fault discrimination logic of the speed sensor, when the fan speed sensor fails, the nonlinear fan speed controller and the nonlinear core engine speed controller are switched in time, so as to always ensure that the deviation of the input parameter measurement value is accurately estimated, the input parameter measurement value is corrected, and finally the input parameter value of the onboard adaptive model is equal to the real engine, thereby solving the safety problem caused by insufficient correction due to the speed sensor failure in the existing onboard adaptive model input parameter correction method, and creating conditions for a high-precision onboard adaptive model.

[0021] Specifically, it is characterized in that the step S2 specifically includes the steps of: S21, preprocessing the relative errors between the calculation-assisted airborne model and the three parameters of the fan speed, core engine speed and high-pressure compressor outlet static pressure of the real engine; S22. Compare the relative errors of the three parameters after preprocessing with a preset threshold value, and determine whether the fan speed sensor and the core engine speed sensor are faulty based on whether the relative error crosses the threshold value, thereby giving a discrimination signal to select the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller for correcting the input parameter measurement value.

[0022] Specifically, the step S21 specifically includes the steps of: S211. Calculate the relative errors between the auxiliary airborne model and the real engine's fan speed (Nf), core engine speed (Nc) and high pressure compressor outlet static pressure:

[0023] Among them, e Nf is the relative error between the actual engine measurement and the fan speed estimated by the onboard model, e Nc is the relative error between the actual engine measurement and the core speed estimated by the onboard model, e ps30 Nf is the relative error between the actual engine measurement and the high pressure compressor outlet static pressure estimated by the onboard model, m is the actual measured value of the real engine fan speed, Nf e is the fan speed estimated by the onboard model, E Nf Nc is the relative error between the actual engine measurement and the fan speed estimated by the onboard model. m is the actual measured value of the actual engine core speed, Nc e is the core engine speed estimated by the onboard model, E Nc is the relative error between the actual engine measurement and the core speed estimated by the onboard model, Ps30 m is the actual measured value of the static pressure at the outlet of the high pressure compressor of the real engine, Ps30 e is the high pressure compressor outlet static pressure estimated by the onboard model, E ps30 is the relative error between the actual engine measurement and the high pressure compressor outlet static pressure estimated by the onboard model; S212, using a first-order low-pass filter to Nf 、e Nc 、e ps30 Perform denoising and obtain the relative error e after filtering. Nf,f 、e Nc,f 、e ps30,f .

[0024] Since the mutual judgment logic design of the speed sensor fault is to use the relative errors between the auxiliary airborne model and the three parameters of the fan speed, core engine speed and high-pressure compressor outlet static pressure of the real engine to formulate the mutual judgment logic to judge whether the parameter measurement value is normal, thereby judging whether the sensor fails, therefore, this embodiment first calculates the relative errors between the auxiliary airborne model and the three parameters of the fan speed (Nf), core engine speed (Nc) and high-pressure compressor outlet static pressure of the real engine and performs filtering and denoising processing to improve the accuracy of the error data.

[0025] Preferably, the step S22 specifically includes the steps of: S221, Initialization: Setting the initial relative error judgment threshold thr = 0.01, selection factor α = 1, sensor fault type sfFlag = 0, where α = 1 indicates that the input parameter measurement deviation calculated by the nonlinear fan speed controller is selected, α = 0 indicates that the input parameter measurement deviation calculated by the nonlinear core speed controller is selected, sfFlag = 0 indicates that the fan speed sensor has no fault; sfFlag = 1 indicates that the fan speed sensor has a fault; sfFlag = 2 indicates that the core speed sensor has a fault; S222. When the fan speed sensor fails, the premise of allowing the value calculated by the nonlinear core engine speed controller as the input parameter measurement value deviation is that the core engine speed sensor is not faulty. Therefore, it is necessary to first determine whether the core engine speed sensor is faulty: when the fan speed sensor fails, if sfFlag = 2, regardless of whether the fan speed sensor fails, the value calculated by the nonlinear core engine speed controller cannot be used as the fuel measurement value deviation. At this time, no matter which fuel measurement value deviation is accurate, the onboard adaptive model fails, and step S2212 is executed; if sfFlag ≠ 2, determine whether the fan speed sensor fails according to the current value of sfFlag, and execute step S223; S223, determine whether α is equal to 1, if so, execute step S224; if not, execute step S228; S224. Judgment e Nf,f of e Nf,f Absolute value | e Nf,f |Is it less than or equal to thr If yes, set α = 1, sfFlag = 0, and execute step S225; S225, Judgment e Nc,f The absolute value of | e Nc,f | thr / 2 and eps30 The absolute value of |e ps30 | is greater than thr? If yes, set α = 0, sfFlag = 1, and execute step S2211; if no, execute step S226; S226, Judgment | e Nc,f |Greater than thr / 2 and |e ps30 |Less than or equal to thr Are all satisfied? If so, set α = 0, sfFlag = 2, and execute step S2211; if not, set α = 1, sfFlag = 0, and execute step S2211; S227, judgement is | e Nc,f |No less than or equal to thr / 2 ; If yes, set α = 0, sfFlag = 1, and execute step S228; if no, execute step S229; S228, judgement |e ps30 |Is it less than or equal to thr If yes, then execute step S2211; if no, then execute step S2212; S229, judgement |e ps30 |Is it less than or equal to thr, If yes, set α = 0, sfFlag = 1, and execute step S2211; if no, set α = 1, sfFlag = 2, and execute step S2211; S2210, judgement | e Nc,f |Is it greater than thr / 2 , if yes, set α = 1, sfFlag = 0; if no, execute step S229; S2211, output α and sfFlag values, and set k = k+1 , propagate the values ​​of α and sfFlag to the next moment, and then execute step S222; S2212, the determination is completed, and a nonlinear fan speed controller or a nonlinear core engine speed controller is selected according to the value of the selection factor α to calculate the input parameter measurement value deviation for correcting the input parameter measurement value.

[0026] The sensor fault mutual judgment method of this embodiment designs a relative error threshold and uses whether the relative errors of the three measurement parameters cross this threshold as a judgment criterion to determine whether the speed sensor is faulty, thereby giving a judgment signal to select a suitable speed controller to calculate the input parameter measurement value deviation to correct the measurement value. The fault mutual judgment method of this embodiment does not need to use complex algorithms for fault diagnosis and identification, but rather performs judgment by mutual judgment of parameters, with a small amount of calculation and a simple and easy-to-implement design.

[0027] Preferably, based on the principle framework of the existing airborne adaptive model input parameter self-correction method, a nonlinear core engine speed (Nc) controller is designed with reference to the design principle of the nonlinear fan speed (Nf) controller. The nonlinear core engine speed (Nc) controller is based on the core engine speed (Nc) measured in real time by the real engine. m ) as a reference value to assist the airborne model in estimating the core engine speed (Nc e ) as the feedback value, and the error between the core engine speed reference value and the feedback value (E Nc ) as input and the measured value of the input parameter (u m ) is used as the feedforward quantity to calculate the input parameter measurement value deviation (Δu c ), this Δu c is the estimated deviation of the measured value of the input parameter.

[0028] In step S2212, a nonlinear fan speed controller or a nonlinear core engine speed controller is selected according to the value of the selection factor α to calculate the input parameter measurement value deviation Δu as: Δu=αΔu f +(1-α) Δu c Among them, Δu f Deviation of the measured value of the input parameter calculated for the nonlinear fan speed controller, Δu c is the measured value deviation of the input parameter calculated by the nonlinear fan speed controller, α is the selection factor, the value range is 0 or 1, and α = 1 selects Δu f Used to correct the measured value of the input parameter, select Δu when α = 0 c Used to correct input parameter measurements.

[0029] Specifically, after the input parameter measurement value deviation Δu is calculated, in step S3, after the autonomous fault-tolerant correction of the input parameters of the onboard adaptive model and the auxiliary onboard model, the input parameter measurement value u actually given to the onboard adaptive model and the auxiliary onboard model is a for: u a =u m +Δu, Among them, u mMeasure the values ​​for the input parameters.

[0030] Preferably, the step S3 further comprises the steps of: When the input parameter measurement values ​​actually given to the airborne adaptive model and the auxiliary airborne model are given to the airborne adaptive model, the sensor fault type sfFlag value and the steady-state signal output by the steady-state discrimination module are used to jointly decide whether to correct the auxiliary airborne model. If sfFlag = 0, no correction is made; if sfFlag = 1, Nf m =Nf e ; If sfFlag = 2, then let Nc m = Nc e ; The switch is closed only when the steady-state signal output by the steady-state discrimination module is steady and only one sensor fault exists, and the outlier-median average filtering algorithm is used to calculate the degradation amount of the health parameter. After filtering Update the auxiliary onboard model, otherwise disconnect the switch and do not update the auxiliary onboard model.

[0031] The measured output parameters used for the airborne adaptive model will be corrected according to the output sensor fault signal to ensure that the airborne adaptive model can still accurately estimate the actual health parameter degradation in the case of a speed sensor failure. At the same time, whether to update the auxiliary airborne model is determined by the steady-state signal output by the steady-state discrimination module and the sensor fault signal to ensure that the auxiliary airborne model health parameters are updated accurately.

[0032] When the fan speed sensor or the core engine speed sensor fails, the fan speed (Nf) / core engine speed (Nc) measurement value is no longer accurate, and there is an estimated residual Δy between the onboard adaptive model and the actual engine measured output parameter, causing the nonlinear Kalman filter to estimate the wrong health parameter degradation amount. This results in the main onboard model and the auxiliary onboard model being unable to track the actual engine status. This application decides whether to correct the sfFlag value obtained. y m Medium m and Nc m If sfFlag = 0, no correction is made; if sfFlag = 1, it indicates that the Nf sensor is faulty, then Nf is set. m = Nf e ; If sfFlag = 2, it means the core engine speed (Nc) sensor is faulty, then Nc m = Nc e By Nf m and Nc m The value correction is obtained y m,c,use y m,c To calculate Δy, we can effectively ensure The accuracy of the sensor is guaranteed, thus ensuring that the true health parameter degradation can be obtained even in the case of sensor failure.

[0033] In this embodiment, whether the health parameters of the auxiliary airborne model are updated is jointly determined by the discrimination signal ssFlag output by the steady-state identification module and the sensor fault signal sfFlag. The steady-state identification module is based on the height H ,Mach number Ma , power rod angle PLA and Nf m Determine whether the current engine state is steady or dynamic. ssFlag == 1 indicates steady state, and ssFlag == 0 indicates dynamic. The switch is closed only when there is a sensor failure in steady state. The outlier-median average filtering algorithm is used to filter the After filtering To update the auxiliary airborne model, in other cases, disconnect the switch and do not update the auxiliary airborne model. The purpose of adding sfFlag to the joint decision is to prevent inaccurate Update auxiliary airborne models (see Figure 3 ).

[0034] This embodiment designs a nonlinear fan speed controller and a nonlinear core engine speed controller respectively, and uses the value of the selection factor α to determine which controller to use to calculate the deviation to correct the input value; when the real measured parameter input value is obtained, it is given to the airborne adaptive model, and at this time, the α value and the steady state are judged to determine whether to correct the auxiliary airborne model. The advantage of this is that due to the large measurement error between the airborne model and the real engine when the sensor fails or is transient, the estimation accuracy is insufficient, so it is not updated when the sensor fails or is transient. This method not only avoids the autonomous fault tolerance of the input parameter measurement value correction, but also realizes the adaptive correction of the auxiliary airborne model.

[0035] like Figure 4 As shown, another preferred embodiment of the present application also provides an autonomous fault-tolerant correction device for input parameters of an aircraft engine onboard adaptive model, comprising: A core engine speed controller establishment module is used to design a nonlinear core engine speed controller based on the existing airborne adaptive model input parameter self-correction method and with reference to the design principle of a nonlinear fan speed controller. The nonlinear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as a reference value, uses the core engine speed estimated by the auxiliary airborne model as a feedback value, uses the error between the reference value and the feedback value as input and uses the input parameter measurement value as a feedforward amount to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation; A sensor fault mutual judgment module is used to perform mutual judgment of the faults of the fan speed sensor and the core engine speed sensor according to the relative errors of the three parameters of the auxiliary airborne model and the real engine, namely, the fan speed, the core engine speed and the high-pressure compressor outlet static pressure, and select the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller to correct the input parameter measurement value through the sensor fault signal output by the fault mutual judgment result; The model autonomous fault-tolerant correction module is used to realize autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model according to the corrected input parameter measurement values ​​when a fan speed sensor or a core engine speed sensor fails.

[0036] like Figure 5 As shown, a preferred embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model in the above-mentioned embodiment when executing the computer program.

[0037] like Figure 6 As shown, the preferred embodiment of the present application also provides a computer device, which can be a terminal or a liveness detection server, and its internal structure diagram can be as shown in Figure 6 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other external computer devices through a network connection. When the computer program is executed by the processor, the steps of the autonomous fault-tolerant correction method of the input parameters of the onboard adaptive model of the aircraft engine are implemented.

[0038] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0039] A preferred embodiment of the present application also provides a storage medium, which includes a stored program. When the program is running, the device where the storage medium is located is controlled to execute the steps of the autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model in the above embodiment.

[0040] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] If the functions described in the method of this embodiment are implemented in the form of software functional units and sold or used as independent products, they can be stored in one or more computing devices readable storage media. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0042] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0043] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0046] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An autonomous fault-tolerant correction method for input parameters of an aircraft engine onboard adaptive model, characterized in that: Includes steps: S1. Based on the existing airborne adaptive model input parameter self-correction method, a nonlinear core engine speed controller is designed with reference to the design principle of a nonlinear fan speed controller. The nonlinear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as a reference value, uses the core engine speed estimated by the auxiliary airborne model as a feedback value, uses the error between the reference value and the feedback value as input, and uses the input parameter measurement value as a feedforward amount to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation; S2. performing mutual fault judgment of the fan speed sensor and the core speed sensor according to the relative errors of the three parameters of the auxiliary airborne model and the real engine, namely, the fan speed, the core speed and the high-pressure compressor outlet static pressure, and selecting the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core speed controller to correct the input parameter measurement value through the sensor fault signal output by the fault mutual judgment result; S3. Implement autonomous fault-tolerant correction of input parameters of the onboard adaptive model and the auxiliary onboard model in the event of a failure of the fan speed sensor or the core engine speed sensor according to the corrected input parameter measurement values.

2. The method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model according to claim 1, characterized in that: The step S2 specifically includes the following steps: S21, preprocessing the relative errors of the three parameters of the fan speed, core engine speed and high pressure compressor outlet static pressure of the calculation-assisted airborne model and the real engine; S22. Compare the relative errors of the three parameters after preprocessing with a preset threshold value, and determine whether the fan speed sensor and the core engine speed sensor are faulty based on whether the relative error crosses the threshold value, thereby giving a discrimination signal to select the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller for correcting the input parameter measurement value.

3. The method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model according to claim 2, characterized in that: The step S21 specifically includes the following steps: S211. Calculate the relative error between the fan speed, core engine speed and high pressure compressor outlet static pressure of the auxiliary airborne model and the real engine: Among them, e Nf is the relative error between the actual engine measurement and the fan speed estimated by the onboard model, e Nc is the relative error between the actual engine measurement and the core speed estimated by the onboard model, e ps30 Nf is the relative error between the actual engine measurement and the high pressure compressor outlet static pressure estimated by the onboard model, m is the actual measured value of the real engine fan speed, Nf e is the fan speed estimated by the onboard model, E Nf Nc is the relative error between the actual engine measurement and the fan speed estimated by the onboard model. m is the actual measured value of the actual engine core speed, Nc e is the core engine speed estimated by the airborne model, E Nc is the relative error between the actual engine measurement and the core speed estimated by the onboard model, Ps30 m is the actual measured value of the static pressure at the outlet of the high pressure compressor of the real engine, Ps30 e is the high pressure compressor outlet static pressure estimated by the onboard model, E ps30 is the relative error between the actual engine measurement and the high pressure compressor outlet static pressure estimated by the onboard model; S212, using a first-order low-pass filter to Nf 、e Nc 、e ps30 Perform denoising and obtain the relative error e after filtering. Nf,f 、e Nc,f 、e ps30,f .

4. The method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model according to claim 2, characterized in that: The step S22 specifically includes the following steps: S221, Initialization: Setting the initial relative error judgment threshold thr = 0.01, selection factor α = 1, sensor fault type sfFlag = 0, where α = 1 indicates that the input parameter measurement deviation calculated by the nonlinear fan speed controller is selected, α = 0 indicates that the input parameter measurement deviation calculated by the nonlinear core speed controller is selected, sfFlag = 0 indicates that the fan speed sensor has no fault; sfFlag = 1 indicates that the fan speed sensor has a fault; sfFlag = 2 indicates that the core speed sensor has a fault; S222, when the fan speed sensor fails, if sfFlag = 2, regardless of whether the fan speed sensor fails, the onboard adaptive model fails, and step S2212 is executed; if sfFlag ≠ 2, determine whether the fan speed sensor fails according to the current value of sfFlag, and execute step S223; S223, determine whether α is equal to 1, if so, execute step S224; if not, execute step S228; S224. Judgment e Nf,f of e Nf,f Absolute value | e Nf,f |Is it less than or equal to thr If yes, set α = 1, sfFlag = 0, and execute step S225; S225, Judgment e Nc,f The absolute value of | e Nc,f | thr / 2 and e ps30 The absolute value of |e ps30 | is greater than thr? If yes, set α = 0, sfFlag = 1, and execute step S2211; if no, execute step S226; S226, Judgment | e Nc,f |Greater than thr / 2 and |e ps30 |Less than or equal to thr Are all satisfied? If so, set α = 0, sfFlag = 2, and execute step S2211; if not, set α = 1, sfFlag = 0, and execute step S2211; S227, judgement is | e Nc,f |No less than or equal to thr / 2 ; If yes, set α = 0, sfFlag = 1, and execute step S228; if no, execute step S229; S228, judgement |e ps30 |Is it less than or equal to thr If yes, then execute step S2211; if no, then execute step S2212; S229, judgement |e ps30 |Is it less than or equal to thr, If yes, set α = 0, sfFlag = 1, and execute step S2211; if no, set α = 1, sfFlag = 2, and execute step S2211; S2210, judgement | e Nc,f |Is it greater than thr / 2 , if yes, set α = 1, sfFlag = 0; if no, execute step S229; S2211, output α and sfFlag values, and set k = k+1 , propagate the values ​​of α and sfFlag to the next moment, and then execute step S222; S2212, the determination is completed, and a nonlinear fan speed controller or a nonlinear core engine speed controller is selected according to the value of the selection factor α to calculate the input parameter measurement value deviation for correcting the input parameter measurement value.

5. The method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model according to claim 4, characterized in that: In step S2212, a nonlinear fan speed controller or a nonlinear core engine speed controller is selected according to the value of the selection factor α to calculate the input parameter measurement value deviation Δu as: Δu=αΔu f +(1-a) Δu c Among them, Δu f Deviation of the measured value of the input parameter calculated for the nonlinear fan speed controller, Δu c Deviations from measured values ​​of input parameters calculated for the nonlinear fan speed controller.

6. The method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model according to claim 5, characterized in that: In step S3, after the autonomous fault-tolerant correction of the input parameters of the onboard adaptive model and the auxiliary onboard model, the input parameter measurement values ​​u of the onboard adaptive model and the auxiliary onboard model are actually given a for: u a =u m +Δu, Among them, u m Measure the values ​​for the input parameters.

7. The method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model according to claim 4, characterized in that: The step S3 further comprises the steps of: When the input parameter measurement values ​​actually given to the airborne adaptive model and the auxiliary airborne model are given to the airborne adaptive model, the sensor fault type sfFlag value and the steady-state signal output by the steady-state discrimination module are used to jointly decide whether to correct the auxiliary airborne model. If sfFlag = 0, no correction is made; if sfFlag = 1, Nf m = Nf e ; If sfFlag = 2, then let Nc m = Nc e ; The switch is closed only when the steady-state signal output by the steady-state discrimination module is steady and only one sensor fault exists, and the outlier-median average filtering algorithm is used to calculate the degradation amount of the health parameter. After filtering Update the auxiliary onboard model, otherwise disconnect the switch and do not update the auxiliary onboard model.

8. An autonomous fault-tolerant correction device for input parameters of an aircraft engine onboard adaptive model, characterized in that: include: A core engine speed controller establishment module is used to design a nonlinear core engine speed controller based on the existing airborne adaptive model input parameter self-correction method and with reference to the design principle of a nonlinear fan speed controller. The nonlinear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as a reference value, uses the core engine speed estimated by the auxiliary airborne model as a feedback value, uses the error between the reference value and the feedback value as input and uses the input parameter measurement value as a feedforward amount to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation; A sensor fault mutual judgment module is used to perform mutual judgment of the faults of the fan speed sensor and the core engine speed sensor according to the relative errors of the three parameters of the auxiliary airborne model and the real engine, namely, the fan speed, the core engine speed and the high-pressure compressor outlet static pressure, and select the input parameter measurement value deviation calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller to correct the input parameter measurement value through the sensor fault signal output by the fault mutual judgment result; The model autonomous fault-tolerant correction module is used to realize autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model according to the corrected input parameter measurement values ​​when a fan speed sensor or a core engine speed sensor fails.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model as claimed in any one of claims 1 to 7 are implemented.

10. A storage medium, comprising a stored program, which controls a device where the storage medium is located to execute the steps of the method for autonomous fault-tolerant correction of input parameters of an aircraft engine onboard adaptive model as claimed in any one of claims 1 to 7 when the program is executed.

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