Autonomous Fault-Tolerant Correction Method, Device, Electronic Equipment and Storage Medium for Input Parameters of Aircraft Engine On-Board Adaptive Model

Abstract

The present application discloses an autonomous fault-tolerant correction method, device, electronic device and storage medium for input parameters of an airborne adaptive model of an aeroengine. The method includes the steps of: S1, designing a non-linear core engine speed controller; S2, performing mutual fault judgment on the fan speed sensor and the core engine speed sensor according to the relative errors of three parameters, namely the fan speed, the core engine speed and the static pressure at the outlet of the high-pressure compressor, between the auxiliary airborne model and the real engine, and selecting the deviation of the input parameter measurement value calculated by the corresponding speed controller through the mutual fault judgment result to correct the input parameter measurement value; S3, realizing the autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model in the case of a fault in the speed sensor according to the corrected input parameter measurement value. The present application solves the safety problem caused by insufficient correction due to the fault of the speed sensor in the existing input parameter correction method, and creates conditions 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 autonomously fault-tolerant correction of input parameters of an airborne adaptive model of an aero-engine. Background Technique

[0002] With the development of the intelligent control system of the new generation of aero-engines, establishing an airborne adaptive model is an important technical means to achieve active control and health diagnosis of the engine at present. However, in the past more than 30 years, the theoretical basic research on the airborne adaptive model technology has been very mature, but existing research all default assumes that the input of the airborne adaptive model is always the same as that of the real engine. However, there is a great measurement uncertainty in the measurement of input parameters such as fuel flow in engineering, and the obtained fuel value has insufficient accuracy. It is very difficult to ensure that the input of the airborne adaptive model is the same as that 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, the problem can be solved by the method for self-correcting input parameters of an airborne adaptive model of an aero-engine (Chen Qian et al., CN202411604208.4). However, this method has a defect that it uses an additionally set auxiliary airborne model and a fan non-linear speed controller to perform online correction on the measured values of the input parameters of the airborne adaptive model of the aero-engine, and updates the auxiliary airborne model by considering the performance degradation of engine components during the whole life cycle. The fan non-linear speed controller calculates the deviation of the measured value of the input parameter by using the measured value of the input parameter as the feedforward quantity, 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. 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 whole 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 deviation of the measured value of the input parameter is not equal to the actual measurement deviation, which further leads to insufficient correction of the measured value of the input parameter and finally causes the failure of the airborne adaptive model. Therefore, in order to effectively realize the engineering application of the method for self-correcting input parameters of an airborne adaptive model of an aero-engine, it is necessary to carry out research on fault-tolerant methods to avoid the risk of insufficient correction of the measured value of the input parameter caused by the sudden failure of the fan speed sensor. Summary of the Invention

[0003] One aspect of the present application provides a method for autonomously fault-tolerant correction of input parameters of an airborne adaptive model of an aero-engine, which is used to solve the problem that the existing method for self-correcting input parameters of an adaptive model has insufficient correction of the measured value of the input parameter and finally causes the failure of the airborne adaptive model.

[0004] The present application is realized through the following solutions:

[0005] An autonomous fault-tolerant correction method for input parameters of an airborne adaptive model of an aero-engine, comprising the steps of:

[0006] S1. On the basis of the existing self-correction method for input parameters of the airborne adaptive model, design a non-linear core engine speed controller by referring to the design principle of the non-linear fan speed controller. The non-linear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as the reference value, and the core engine speed estimated by the auxiliary airborne model as the feedback value. The deviation of the input parameter measurement value is calculated as the estimated deviation of the input parameter measurement value by using the error between the reference value and the feedback value as the input and the measured value of the input parameter as the feedforward quantity.

[0007] S2. Perform mutual fault judgment on the fan speed sensor and the core engine speed sensor according to the relative errors of the three parameters of the fan speed, core engine speed and static pressure at the outlet of the high-pressure compressor between the auxiliary airborne model and the real engine. Select the deviation of the input parameter measurement value calculated by the non-linear fan speed controller or the non-linear core engine speed controller according to the sensor fault signal output by the mutual fault judgment result to correct the input parameter measurement value.

[0008] S3. Realize the autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model in the case of a fault in the fan speed sensor or the core engine speed sensor according to the corrected input parameter measurement value.

[0009] Further, it is characterized in that the step S2 specifically includes the steps of:

[0010] S21. Calculate the relative errors of the three parameters of the fan speed, core engine speed and static pressure at the outlet of the high-pressure compressor between the auxiliary airborne model and the real engine for preprocessing.

[0011] S22. Compare the relative errors of the three preprocessed parameters with a preset threshold, and determine whether the fan speed sensor and the core engine speed sensor are faulty according to whether the relative error crosses the threshold, so as to give a discrimination signal to select the deviation of the input parameter measurement value calculated by the non-linear fan speed controller or the non-linear core engine speed controller to correct the input parameter measurement value.

[0012] Further, the step S21 specifically includes the steps of:

[0013] S211. Calculate the relative errors of the three parameters of the fan speed, core engine speed and static pressure at the outlet of the high-pressure compressor between the auxiliary airborne model and the real engine:

[0014]

[0015] where eNf is the relative error between the true engine measurement and the fan speed estimated by the on-board model, e Nc is the relative error between the true engine measurement and the core speed estimated by the on-board model, e ps30 is the relative error between the true engine measurement and the static pressure at the outlet of the high-pressure compressor estimated by the on-board model, Nf m is the actual measured value of the true engine fan speed, Nf e is the fan speed estimated by the on-board model, E Nf is the relative error between the true engine measurement and the fan speed estimated by the on-board model, Nc m is the actual measured value of the true engine core speed, Nc e is the core speed estimated by the on-board model, E Nc is the relative error between the true engine measurement and the core speed estimated by the on-board model, Ps30 m is the actual measured value of the true engine static pressure at the outlet of the high-pressure compressor, Ps30 e is the static pressure at the outlet of the high-pressure compressor estimated by the on-board model, E ps30 is the relative error between the true engine measurement and the static pressure at the outlet of the high-pressure compressor estimated by the on-board model;

[0016] S212. Use a first-order low-pass filter to perform denoising processing on e Nf , e Nc , e ps30 respectively, and obtain the filtered relative errors e Nf,f , e Nc,f , e ps30,f .

[0017] Furthermore, the step S22 specifically includes the steps:

[0018] S221. Initialization: Set the initial relative error judgment threshold thr = 0.01, select the factor α = 1, and the sensor fault type sfFlag = 0, where α = 1 indicates selecting the measurement deviation of the input parameters calculated by the nonlinear fan speed controller, α = 0 indicates selecting the measurement deviation of the input parameters calculated by the nonlinear core speed controller, 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;

[0019] S222. When the fan speed sensor fails, if sfFlag = 2, regardless of whether the fan speed sensor fails or not, the on-board adaptive model fails, and step S2212 is executed; if sfFlag ≠ 2, it is determined whether the fan speed sensor fails according to the current value of sfFlag, and step S223 is executed;

[0020] S223. Determine whether α is equal to 1. If so, step S224 is executed; if not, step S228 is executed;

[0021] S224. Determine e Nf,f of e Nf,f absolute value | e Nf,f | is less than or equal to thr . If so, let α = 1, sfFlag = 0, and step S225 is executed;

[0022] S225. Determine e Nc,f absolute value large | e Nc,f | is greater than thr / 2 and e ps30 absolute value |e ps30 | greater than thr are both satisfied. If so, let α = 0, sfFlag = 1, and step S2211 is executed; if not, step S226 is executed;

[0023] S226. Determine | e Nc,f | is greater than thr / 2 and |e ps30 | is less than or equal to thr are both satisfied; if so, let α = 0, sfFlag = 2, and step S2211 is executed; if not, let α = 1, sfFlag = 0, and step S2211 is executed;

[0024] S227. Determine whether is| e Nc,f | less than or equal to thr / 2 ; if so, let α = 0, sfFlag = 1, and step S228 is executed; if not, step S229 is executed;

[0025] S228. Determine |e ps30 | is less than or equal to thr , if so, step S2211 is executed; if not, step S2212 is executed;

[0026] S229. Determine |e ps30 | is less than or equal tothr, If so, set α = 0 and sfFlag = 1, then execute step S2211; if not, set α = 1 and sfFlag = 2, then execute step S2211;

[0027] S2210. Determine whether | e Nc,f | is greater than thr / 2 . If so, set α = 1 and sfFlag = 0; if not, execute step S229;

[0028] S2211. Output the values of α and sfFlag, and set k = k+1 . Propagate the values of α and sfFlag to the next moment, then execute step S222;

[0029] S2212. The discrimination ends. Select the non - linear fan speed controller or the non - linear core machine speed controller according to the value of the selection factor α to calculate the deviation of the input parameter measurement value for correcting the input parameter measurement value.

[0030] Further, in step S2212, the deviation Δu of the input parameter measurement value calculated by selecting the non - linear fan speed controller or the non - linear core machine speed controller according to the value of the selection factor α is:

[0031] Δu = αΔu f +(1 - α)Δu c

[0032] where, Δu f is the deviation of the input parameter measurement value calculated by the non - linear fan speed controller, and Δu c is the deviation of the input parameter measurement value calculated by the non - linear fan speed controller.

[0033] Further, in step S3, after the autonomous fault - tolerance correction of the input parameters of the airborne adaptive model and the auxiliary airborne model, the actual input parameter measurement value u a given to the airborne adaptive model and the auxiliary airborne model is:

[0034] u a = u m +Δu,

[0035] where, u m is the input parameter measurement value.

[0036] Further, step S3 also includes the steps:

[0037] When the measured values of the input parameters actually given to the airborne adaptive model and the auxiliary airborne model are obtained and given to the airborne adaptive model, at this time, it is jointly determined whether to correct the auxiliary airborne model by judging the value of the sensor fault type sfFlag and the steady-state signal output by the steady-state discrimination module. If sfFlag = 0, no correction is made; if sfFlag = 1, then let Nf m = Nf e ; if sfFlag = 2, then let Nc m = Nc e ; Only when the steady-state signal output by the steady-state discrimination module is in a steady state and there is only one sensor fault, the switch is closed, and the outlier-median average filtering algorithm is used to filter the degradation amount of the health parameter filtered to update the auxiliary airborne model, otherwise the switch is opened and the auxiliary airborne model is not updated.

[0038] On the other hand, the present application also provides an autonomous fault-tolerant correction device for the input parameters of an aero-engine airborne adaptive model, including:

[0039] A core engine speed controller establishment module, which is used to design a non-linear core engine speed controller based on the existing self-correction method of the input parameters of the airborne adaptive model and referring to the design principle of the non-linear fan speed controller. The non-linear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as the reference value, and the core engine speed estimated by the auxiliary airborne model as the feedback value. The error between the reference value and the feedback value is used as the input, and the measured value of the input parameter is used as the feedforward quantity to calculate the deviation of the measured value of the input parameter as the estimated deviation of the measured value of the input parameter;

[0040] A sensor fault mutual judgment module, which is used to perform mutual fault judgment on the fan speed sensor and the core engine speed sensor according to the relative errors of the three parameters of the fan speed, the core engine speed, and the static pressure at the outlet of the high-pressure compressor between the auxiliary airborne model and the real engine. Through the sensor fault signal output by the fault mutual judgment result, the deviation of the measured value of the input parameter calculated by the non-linear fan speed controller or the non-linear core engine speed controller is selected to correct the measured value of the input parameter;

[0041] A model autonomous fault-tolerant correction module, which is used to realize the autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model in the case of a fault in the fan speed sensor or the core engine speed sensor according to the corrected measured value of the input parameter.

[0042] On the other hand, the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the method for autonomously fault-tolerantly correcting the input parameters of the aero-engine on-board adaptive model are implemented.

[0043] On the other hand, the present application also provides a storage medium. The storage medium includes a stored program, and when the program runs, it controls the device where the storage medium is located to execute the steps of the method for autonomously fault-tolerantly correcting the input parameters of the aero-engine on-board adaptive model.

[0044] Compared with the prior art, the present application has the following beneficial effects:

[0045] The present invention provides a method for autonomously fault-tolerantly correcting the input parameters of an aero-engine on-board adaptive model. The method for autonomously fault-tolerantly correcting the input parameters of the aero-engine on-board adaptive model improves the autonomous fault-tolerant ability of correcting the measured values of the input parameters of the on-board adaptive model by designing a dual-redundancy control strategy for the fan speed and the core engine speed. Based on the mutual discrimination of the faults of the speed sensors, when a fault occurs in the fan speed sensor, the nonlinear fan speed controller and the nonlinear core engine speed controller are switched in time, so as to always ensure the accurate estimation of the deviation of the measured value of the input parameter, correct the measured value of the input parameter, and finally make the value of the input parameter of the on-board adaptive model equal to that of the real engine, solving the safety problem brought by insufficient correction due to the fault of the speed sensor in the existing method for correcting the input parameters of the on-board adaptive model, and creating conditions for a high-precision on-board adaptive model.

[0046] In addition to the purposes, features, and advantages described above, the present application has other purposes, features, and advantages. The following will refer to the drawings for a further detailed description of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application:

[0048] Figure 1 is a schematic diagram of the process for autonomously fault-tolerantly correcting the input parameters of the aero-engine on-board adaptive model in the preferred embodiment of the present application;

[0049] Figure 2 is a schematic diagram of the sub-step process of step S2;

[0050] Figure 3 is a schematic diagram of the principle of the method for autonomously fault-tolerantly correcting the input parameters of the aero-engine on-board adaptive model in the preferred embodiment of the present application;

[0051] Figure 4 Schematic diagram of the module of the autonomous fault-tolerant correction device for the input parameters of the aero-engine airborne adaptive model in the preferred embodiment of the present application;

[0052] Figure 5 Schematic block diagram of the electronic device entity in the preferred embodiment of the present application;

[0053] Figure 6 Internal structure diagram of the computer device in the preferred embodiment of the present application. Detailed implementation manners

[0054] The embodiments of the present application will be described in detail below with reference to the accompanying drawings, but the present application can be implemented in many different ways defined and covered by the following.

[0055] As Figure 1 shown, the preferred embodiment of the present application provides an autonomous fault-tolerant correction method for the input parameters of the aero-engine airborne adaptive model, including the steps of:

[0056] S1. On the basis of the existing self-correction method for the input parameters of the airborne adaptive model, a non-linear core engine speed controller is designed by referring to the design principle of the non-linear fan speed controller. The non-linear core engine speed controller uses the core engine speed measured in real time by the core engine speed sensor of the real engine as the reference value, and the core engine speed estimated by the auxiliary airborne model as the feedback value. The deviation of the input parameter measurement value is calculated as the estimated deviation of the input parameter measurement value by using the error between the reference value and the feedback value as the input and the measured value of the input parameter as the feed-forward quantity;

[0057] S2. Fault mutual judgment of the fan speed sensor and the core engine speed sensor is performed according to the relative errors of the three parameters of the fan speed, core engine speed, and static pressure at the outlet of the high-pressure compressor of the auxiliary airborne model and the real engine. The measured value deviation of the input parameter calculated by the non-linear fan speed controller or the non-linear core engine speed controller is selected through the sensor fault signal output by the fault mutual judgment result to correct the measured value of the input parameter;

[0058] S3. Autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model is realized according to the corrected measured value of the input parameter in the case of a fault in the fan speed sensor or the core engine speed sensor.

[0059] Because the method for constructing the current airborne adaptive model of aeroengines assumes that the sensor measurements are not faulty, once a measurement fault occurs, the correction of the airborne adaptive model will deviate from the state of the real engine or even fail. That is, since the self-correction method of the input parameters of the airborne adaptive model mainly compensates for the lack of measurement accuracy of the input parameters by controlling the fan speed to match the real engine speed, and highly depends on the measurement confidence of the fan speed sensor for the fan speed. Therefore, this embodiment provides a method for autonomously fault-tolerant correction of the input parameters of the airborne adaptive model of aeroengines. This method for autonomously fault-tolerant correction of the input parameters of the airborne adaptive model of aeroengines improves the autonomous fault-tolerant ability of the correction of the measured values of the input parameters of the airborne adaptive model by designing a dual-redundancy control strategy for the fan speed and the core engine speed. Based on the mutual fault discrimination logic of the speed sensors, when the fan speed sensor fails, it realizes the timely switching between the nonlinear fan speed controller and the nonlinear core engine speed controller, so as to always ensure the accurate estimation of the deviation of the measured value of the input parameter, correct the measured value of the input parameter, and finally make the value of the input parameter of the airborne adaptive model equal to that of the real engine, solving the safety problem caused by insufficient correction due to speed sensor faults in the existing method for correcting the input parameters of the airborne adaptive model, and creating conditions for a high-precision airborne adaptive model.

[0060] Specifically, it is characterized in that the step S2 specifically includes the steps:

[0061] S21. Calculate the relative errors of the three parameters of the fan speed, the core engine speed, and the static pressure at the outlet of the high-pressure compressor of the auxiliary airborne model and the real engine for preprocessing;

[0062] S22. Compare the relative errors of the three preprocessed parameters with a preset threshold, and determine whether the fan speed sensor and the core engine speed sensor are faulty according to whether the relative error crosses the threshold, so as to give a discrimination signal to select the deviation of the measured value of the input parameter calculated by the nonlinear fan speed controller or the nonlinear core engine speed controller for correcting the measured value of the input parameter.

[0063] Specifically, the step S21 specifically includes the steps:

[0064] S211. Calculate the relative errors of the three parameters of the fan speed (Nf), the core engine speed (Nc), and the static pressure at the outlet of the high-pressure compressor of the auxiliary airborne model and the real engine:

[0065]

[0066] Wherein, e Nf is the relative error between the measured value of the real engine and the estimated fan speed of the airborne model, e Ncis the relative error between the measured value of the real engine and the core speed estimated by the on-board model, e ps30 is the relative error between the measured value of the real engine and the static pressure at the outlet of the high-pressure compressor estimated by the on-board model, Nf m is the actual measured value of the real engine fan speed, Nf e is the fan speed estimated by the on-board model, E Nf is the relative error between the measured value of the real engine and the fan speed estimated by the on-board model, Nc m is the actual measured value of the real engine core speed, Nc e is the core speed estimated by the on-board model, E Nc is the relative error between the measured value of the real engine and the core speed estimated by the on-board model, Ps30 m is the actual measured value of the real engine static pressure at the outlet of the high-pressure compressor, Ps30 e is the static pressure at the outlet of the high-pressure compressor estimated by the on-board model, E ps30 is the relative error between the measured value of the real engine and the static pressure at the outlet of the high-pressure compressor estimated by the on-board model;

[0067] S212. Use a first-order low-pass filter to perform denoising processing on e Nf and e Nc and e ps30 respectively, and obtain the filtered relative errors e Nf,f and e Nc,f and e ps30,f .

[0068] Since the design of the speed sensor fault mutual judgment logic uses the relative errors of three parameters, namely the fan speed, core speed, and static pressure at the outlet of the high-pressure compressor, between the auxiliary on-board model and the real engine to formulate the mutual judgment logic to determine whether the parameter measurement value is normal, so as to determine whether the sensor has a fault. Therefore, in this embodiment, the relative errors of the three parameters, namely the fan speed (Nf), core speed (Nc), and static pressure at the outlet of the high-pressure compressor, between the auxiliary on-board model and the real engine are first calculated and filtered and denoised to improve the accuracy of the error data.

[0069] Preferably, the step S22 specifically includes the steps:

[0070] S221. Initialization: Set the initial relative error judgment threshold thr= 0.01, select factor α = 1, sensor fault type sfFlag = 0, where α = 1 indicates selecting the measurement deviation of the input parameters calculated by the non - linear fan speed controller, α = 0 indicates selecting the measurement deviation of the input parameters calculated by the non - linear core engine speed controller, and 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 engine speed sensor has a fault;

[0071] S222. When the fan speed sensor fails, allowing the value calculated by the non - linear core engine speed controller to be used as the measurement value deviation of the input parameter on the premise that the core engine speed sensor has no fault. Therefore, first, it is necessary to determine whether the core engine speed sensor has a fault: When the fan speed sensor fails, if sfFlag = 2, regardless of whether the fan speed sensor has a fault or not, the value calculated by the non - linear core engine speed controller cannot be used as the fuel measurement value deviation. At this time, no matter which fuel measurement value deviation is inaccurate, so the on - board adaptive model fails, and step S2212 is executed; if sfFlag ≠ 2, determine whether the fan speed sensor has a fault according to the current value of sfFlag, and execute step S223;

[0072] S223. Determine whether α is equal to 1. If so, execute step S224; if not, execute step S228;

[0073] S224. Determine e Nf,f of e Nf,f absolute value | e Nf,f | is less than or equal to thr , if so, let α = 1, sfFlag = 0, and execute step S225;

[0074] S225. Determine e Nc,f absolute value of | e Nc,f | is greater than thr / 2 and e ps30 absolute value of |e ps30 | greater than thr are both satisfied. If so, let α = 0, sfFlag = 1, and execute step S2211; if not, execute step S226;

[0075] S226. Determine | e Nc,f | is greater than thr / 2 and |e ps30 | is less than or equal to thrWhether all are satisfied; if so, let α = 0, sfFlag = 2, and execute step S2211; if not, let α = 1, sfFlag = 0, and execute step S2211;

[0076] S227. Judge whether| e Nc,f |is less than or equal to thr / 2 ; if so, let α = 0, sfFlag = 1, and execute step S228; if not, execute step S229;

[0077] S228. Judge whether|e ps30 |is less than or equal to thr ; if so, execute step S2211; if not, execute step S2212;

[0078] S229. Judge whether|e ps30 |is less than or equal to thr, If so, let α = 0, sfFlag = 1, and then execute step S2211; if not, let α = 1, sfFlag = 2, and then execute step S2211;

[0079] S2210. Judge whether| e Nc,f |is greater than thr / 2 , if so, let α = 1, sfFlag = 0; if not, execute step S229;

[0080] S2211. Output the values of α and sfFlag, and let k = k+1 , propagate the values of α and sfFlag to the next moment, and then execute step S222;

[0081] S2212. The discrimination ends. According to the value of the selection factor α, select the non - linear fan speed controller or the non - linear core engine speed controller to calculate the deviation of the input parameter measurement value for correcting the input parameter measurement value.

[0082] The sensor fault mutual discrimination method of this embodiment determines whether the speed sensor is faulty by designing a relative error threshold and using whether the relative errors of 3 measurement parameters cross this threshold as a judgment criterion, so as to give a discrimination signal to select a suitable speed controller to calculate the deviation of the input parameter measurement value to correct the measurement value. The fault mutual discrimination method of this embodiment does not need to use complex algorithms for fault diagnosis and identification, but discriminates through the way of parameter mutual discrimination, with small calculation amount and simple design and easy implementation.

[0083] Preferably, based on the principle architecture of the existing on-board adaptive model input parameter self-correction method, a non-linear core speed (Nc) controller is designed by referring to the design principle of the non-linear fan speed (Nf) controller. The non-linear core speed (Nc) controller uses the core speed (Nc m ) measured in real time by the actual engine as the reference value, and uses the core speed (Nc e ) estimated by the auxiliary on-board model as the feedback value. By using the error (E Nc ) between the core speed reference value and the feedback value as the input and the measured value of the input parameter (u m ) as the feedforward quantity, the deviation of the measured value of the input parameter (Δu c ) is calculated. This Δu c is the estimated deviation of the measured value of the input parameter.

[0084] In step S2212, according to the value of the selection factor α, the non-linear fan speed controller or the non-linear core speed controller is selected to calculate the deviation Δu of the measured value of the input parameter as:

[0085] Δu = αΔu f +(1 - α)Δu c

[0086] where, Δu f is the deviation of the measured value of the input parameter calculated by the non-linear fan speed controller, Δu c is the deviation of the measured value of the input parameter calculated by the non-linear fan speed controller, α is the selection factor, and its value range is 0 or 1. When α = 1, Δu f is selected to correct the measured value of the input parameter. When α = 0, Δu c is selected to correct the measured value of the input parameter.

[0087] Specifically, after calculating the deviation Δu of the measured value of the input parameter, in step S3, after the autonomous fault tolerance correction of the input parameters of the on-board adaptive model and the auxiliary on-board model, the measured value u of the input parameter actually given to the on-board adaptive model and the auxiliary on-board model a is:

[0088] u a = u m + Δu,

[0089] where, u m is the measured value of the input parameter.

[0090] Preferably, step S3 further includes the steps:

[0091] When the measured values of the input parameters actually given to the airborne adaptive model and the auxiliary airborne model are obtained and given to the airborne adaptive model, at this time, it is jointly determined by judging the value of the sensor fault type sfFlag and the steady-state signal output by the steady-state discrimination module whether to correct the auxiliary airborne model. If sfFlag = 0, no correction is made; if sfFlag = 1, then let Nf m = Nf e ; if sfFlag = 2, then let Nc m = Nc e ; Only when the steady-state signal output by the steady-state discrimination module is in a steady state and there is only one sensor fault, the switch is closed, and the outlier-median average filtering algorithm is used to filter the degradation amount of the health parameter filtered to update the auxiliary airborne model. Otherwise, the switch is disconnected and the auxiliary airborne model is not updated.

[0092] The measured output parameters 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 true degradation amount of the health parameter in the case of a speed sensor fault. At the same time, whether to update the auxiliary airborne model is jointly determined by the steady-state signal output by the steady-state discrimination module and the sensor fault signal to ensure accurate update of the health parameter of the auxiliary airborne model.

[0093] When the fan speed sensor or the core engine speed sensor fails, the measured values of the fan speed (Nf) / core engine speed (Nc) are no longer accurate, and there is an estimation residual Δy between the airborne adaptive model and the measured output parameters of the real engine, resulting in an incorrect degradation amount of the health parameter estimated by the non-linear Kalman filter , which causes the main airborne model and the auxiliary airborne model not to track the real engine state. This application decides whether to correct y m Nf m and Nc m in according to the obtained value of sfFlag: If sfFlag == 0, no correction is made; if sfFlag = 1 indicating that the Nf sensor fails, then let Nf m = Nf e ; if sfFlag = 2 indicating that the core engine speed (Nc) sensor fails, then let Nc m = Nc e . By correcting the values of Nf m and Nc m , obtain y m,c , and use y m,c to calculate Δy can effectively ensure accuracy, so as to ensure that the true degradation amount of the health parameter can still be obtained in the case of sensor failure.

[0094] In this embodiment, whether the health parameter of the auxiliary aircraft-borne model is updated is jointly determined by the discrimination signal ssFlag and the sensor failure signal sfFlag output by the steady-state recognition module. The steady-state recognition module consists of altitude H , Mach number Ma , power lever angle PLA, and Nf m jointly determine whether the current engine state is steady state or dynamic state. ssFlag == 1 indicates steady state, and ssFlag == 0 indicates dynamic state. Only when it is in the steady state and there is at most one sensor failure, the switch is closed, and the outlier-median average filtering algorithm is used to filtered to update the auxiliary aircraft-borne model. In other cases, the switch is disconnected and the auxiliary aircraft-borne model is not updated. The purpose of jointly determining with sfFlag is to prevent inaccurate updating the auxiliary aircraft-borne model when both the Nf and Nc sensors fail (see Figure 3 ).

[0095] In this embodiment, by separately designing a non-linear fan speed controller and a non-linear core speed controller, the value of the selection factor α is used to determine which controller's calculated deviation is used to correct the input value; when the true measured parameter input value is obtained and given to the aircraft-borne adaptive model, at this time, whether to correct the auxiliary aircraft-borne model is determined by judging the value of α and the steady state. The advantage of this is that due to sensor failure or transient state, the measurement error between the aircraft-borne model and the real engine is large and the estimation accuracy is insufficient, so it is not updated during sensor failure and transient state. This method not only avoids the autonomous fault tolerance of the correction of the input parameter measurement value, but also realizes the adaptive correction of the auxiliary aircraft-borne model.

[0096] As Figure 4 shown, another preferred embodiment of the present application also provides an aircraft-borne adaptive model input parameter autonomous fault tolerance correction device for an aeroengine, including:

[0097] A core speed controller establishment module, which is used to design a non-linear core speed controller by referring to the design principle of the non-linear fan speed controller on the basis of the existing aircraft-borne adaptive model input parameter self-correction method. The non-linear core speed controller uses the core speed measured in real time by the core speed sensor of the real engine as the reference value, and the core speed estimated by the auxiliary aircraft-borne model as the feedback value. The error between the reference value and the feedback value is used as the input, and the input parameter measurement value is used as the feedforward quantity to calculate the input parameter measurement value deviation as the estimated input parameter measurement value deviation;

[0098] The sensor fault mutual judgment module is used to perform mutual fault judgment on the fan speed sensor and the core speed sensor according to the relative errors of three parameters, namely, the fan speed, the core speed, and the static pressure at the outlet of the high-pressure compressor of the auxiliary aircraft model and the real engine. Through the sensor fault signal output by the mutual fault judgment result, the measured value deviation of the input parameters calculated by the non-linear fan speed controller or the non-linear core speed controller is selected to correct the measured value of the input parameters;

[0099] The model autonomous fault tolerance correction module is used to realize the autonomous fault tolerance correction of the input parameters of the aircraft-borne adaptive model and the auxiliary aircraft-borne model in the case of a fault in the fan speed sensor or the core speed sensor according to the corrected measured value of the input parameters.

[0100] As Figure 5 shown, a preferred embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the method for autonomous fault tolerance correction of the input parameters of the aero-engine aircraft-borne adaptive model in the above embodiment are realized.

[0101] As Figure 6 shown, a preferred embodiment of the present application also provides a computer device, which can be a terminal or a living body detection server, and its internal structure diagram can be as Figure 6 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 method for autonomous fault tolerance correction of the input parameters of the aero-engine aircraft-borne adaptive model are realized.

[0102] Those skilled in the art can understand that Figure 6 the structure shown in

[0103] is only a block diagram of some structures 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 some components, or have different component arrangements. A preferred embodiment of the present application also provides a storage medium, the storage medium includes a stored program, and when the program runs, it controls the device where the storage medium is located to execute the steps of the method for autonomous fault tolerance correction of the input parameters of the aero-engine aircraft-borne adaptive model in the above embodiment.

[0104] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0105] When the functions described in the method of this embodiment are implemented in the form of software function units and sold or used as independent products, they can be stored in one or more computer-readable storage media that can be read by a computing device. Based on such an understanding, the part that contributes to the prior art or the part of this technical solution in the embodiments of this application can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions for causing a computing device (which can be a personal computer, a server, a mobile computing device, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0106] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program codes. The solutions in the embodiments of this application can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0107] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0110] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0111] 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 equivalent technologies, 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 airborne adaptive model of an aeroengine, characterized in that Including the steps: S1. On the basis of the existing aircraft-borne adaptive model input parameter self-correction method, design a non-linear core engine speed controller by referring to the design principle of the non-linear fan speed controller. The non-linear core engine speed controller takes the core engine speed measured in real time by the core engine speed sensor of the real engine as the reference value, takes the core engine speed estimated by the auxiliary aircraft-borne model as the feedback value, calculates the deviation of the input parameter measurement value as the estimated deviation of the input parameter measurement value by using the error between the reference value and the feedback value as the input and the input parameter measurement value as the feedforward quantity; S2. Coordinate and cooperate according to the relative errors of the three parameters of the fan speed, core engine speed and static pressure at the outlet of the high-pressure compressor of the auxiliary aircraft-borne model and the real engine to perform mutual fault judgment on the fan speed sensor and the core engine speed sensor. Select the deviation of the input parameter measurement value calculated by the non-linear fan speed controller or the non-linear core engine speed controller according to the sensor fault signal output by the mutual fault judgment result to correct the input parameter measurement value; S3. Realize the autonomous fault-tolerant correction of the input parameters of the aircraft-borne adaptive model and the auxiliary aircraft-borne model in the case of a fault in the fan speed sensor or the core engine speed sensor according to the corrected input parameter measurement value.

2. The autonomous fault-tolerant correction method for the input parameters of the airborne adaptive model of an aeroengine according to claim 1, characterized in that The specific steps of step S2 include: S21. Calculate the relative errors of the three parameters of the fan speed, core engine speed and static pressure at the outlet of the high-pressure compressor of the auxiliary aircraft-borne model and the real engine for preprocessing; S22. Compare the relative errors of the three preprocessed parameters with the preset threshold value, and determine whether the fan speed sensor and the core engine speed sensor are faulty according to whether the relative error crosses the threshold value, so as to give a discrimination signal to select the deviation of the input parameter measurement value calculated by the non-linear fan speed controller or the non-linear core engine speed controller to correct the input parameter measurement value.

3. The autonomous fault-tolerant correction method for the input parameters of the airborne adaptive model of an aeroengine according to claim 2, wherein, The specific steps of step S21 include: S211. Calculate the relative errors of the three parameters of the fan speed, core engine speed and static pressure at the outlet of the high-pressure compressor of the auxiliary aircraft-borne model and the real engine: Among them, e Nf is the relative error between the true engine measurement value and the fan speed estimated by the on-board model, e Nc is the relative error between the true engine measurement value and the core speed estimated by the on-board model, e ps30 is the relative error between the true engine measurement value and the static pressure at the outlet of the high-pressure compressor estimated by the on-board model, Nf m is the actual measured value of the true engine fan speed, Nf e is the fan speed estimated by the on-board model, E Nf is the relative error between the true engine measurement value and the fan speed estimated by the on-board model, Nc m is the actual measured value of the true engine core speed, Nc e is the core speed estimated by the on-board model, E Nc is the relative error between the true engine measurement value and the core speed estimated by the on-board model, Ps30 m is the actual measured value of the true engine static pressure at the outlet of the high-pressure compressor, Ps30 e is the static pressure at the outlet of the high-pressure compressor estimated by the on-board model, E ps30 is the relative error between the true engine measurement value and the static pressure at the outlet of the high-pressure compressor estimated by the on-board model; S212. Use a first-order low-pass filter to perform denoising processing on e Nf , e Nc , e ps30 respectively, and obtain the filtered relative errors e Nf,f , e Nc,f , e ps30,f .

4. The autonomous fault-tolerant correction method for the input parameters of the airborne adaptive model of an aero-engine according to claim 2, characterized in that, The specific steps of step S22 include: S221. Initialization: Set the initial relative error judgment threshold thr = 0.01, select the factor α = 1, and the sensor fault type sfFlag = 0. Among them, α = 1 means selecting the input parameter measurement deviation calculated by the non-linear fan speed controller, α = 0 means selecting the input parameter measurement deviation calculated by the non-linear core engine speed controller, and sfFlag = 0 means that the fan speed sensor has no fault; sfFlag = 1 means that the fan speed sensor is faulty; sfFlag = 2 means that the core engine speed sensor is faulty; S222. When the fan speed sensor fails, if sfFlag = 2, regardless of whether the fan speed sensor fails, the aircraft-borne adaptive model fails, and step S2212 is executed; if sfFlag ≠ 2, judge whether the fan speed sensor fails according to the current value of sfFlag, and execute step S223; S223. Judge whether α is equal to 1. If so, execute step S224; if not, execute step S227; S224. Determine e Nf,f whether the absolute value of |e Nf,f | is less than or equal to thr. If so, set α = 1, sfFlag = 0, and execute step S225; S225. Determine e Nc,f Whether the absolute value of |e Nc,f | is greater than thr / 2 and the absolute value of e ps30 | is greater than thr are both satisfied. If so, let α = 0, sfFlag = 1, and execute step S2211; if not, execute step S226; ps30 | is greater than thr are both satisfied. If so, let α = 0, sfFlag = 1, and execute step S2211; if not, execute step S226; S226. Determine whether |e Nc,f | is greater than thr / 2 and |e ps30 | is less than or equal to thr are both satisfied; if so, set α = 0, sfFlag = 2, and execute step S2211; if not, set α = 1, sfFlag = 0, and execute step S2211; S227. Determine whether |e Nc,f | is less than or equal to thr / 2; if so, set α = 0, sfFlag = 1, and execute step S228; if not, execute step S229; S228, Determine if |e ps30 | is less than or equal to thr. If so, execute step S2211; if not, execute step S2212; S229. Determine if |e ps30 | is less than or equal to thr. If so, set α = 0, sfFlag = 1, and then execute step S2211; if not, set α = 1, sfFlag = 2, and then execute step S2211; S2210. Determine whether |e Nc,f is greater than thr / 2. If so, set α = 1 and sfFlag = 0; if not, execute step S229; Output the values of α and sfFlag, and let k = k + 1. Propagate the values of α and sfFlag to the next moment, then execute step S222; The discrimination ends. According to the value of the selection factor α, select the non-linear fan speed controller or the non-linear core speed controller to calculate the deviation of the input parameter measurement value for correcting the input parameter measurement value.

5. The method for autonomously fault-tolerant correction of input parameters of an airborne adaptive model of an aeroengine according to claim 4, characterized in that, In step S2212, according to the value of the selection factor α, select the non-linear fan speed controller or the non-linear core speed controller to calculate the deviation Δu of the input parameter measurement value as follows: Δu = αΔu f +(1 - α)Δu c where, Δu f is the deviation of the measured value of the input parameter calculated by the non-linear fan speed controller, and Δu c is the deviation of the measured value of the input parameter calculated by the non-linear core engine speed controller.

6. The method for autonomously fault-tolerant correction of input parameters of an airborne adaptive model of an aeroengine according to claim 5, characterized in that, In step S3, after the autonomous fault tolerance correction of the input parameters of the airborne adaptive model and the auxiliary airborne model, the measured value u of the input parameters actually given to the airborne adaptive model and the auxiliary airborne model a is as follows: u a = u m + Δu, Among them, u m is the measured value of the input parameter.

7. The autonomous fault-tolerant correction method for the input parameters of the airborne adaptive model of an aeroengine according to claim 4, characterized in that Step S3 further includes the steps: When obtaining the measured values of the input parameters actually given to the airborne adaptive model and the auxiliary airborne model and feeding them to the airborne adaptive model, at this time, it is jointly determined whether to correct the auxiliary airborne model by judging the value of the sensor fault type sfFlag and the steady-state signal output by the steady-state discrimination module. If sfFlag = 0, no correction is made; if sfFlag = 1, then let Nf m = Nf e ; if sfFlag = 2, then let Nc m = Nc e ; Only when the steady-state signal output by the steady-state discrimination module is in a steady state and there is only one sensor fault, the switch is closed, and the outlier-median average filtering algorithm is used to filter the degradation amount of the health parameter After filtering to update the auxiliary airborne model, otherwise the switch is opened and the auxiliary airborne model is not updated.

8. An on-board adaptive model input parameter autonomous fault-tolerant correction device for an aero-engine, characterized in that Including: A core speed controller establishment module, which is used to design a non-linear core speed controller based on the design principle of the non-linear fan speed controller on the basis of the existing method for self-correcting the input parameters of the airborne adaptive model. The non-linear core speed controller uses the core speed measured by the core speed sensor of the real engine in real time as the reference value, and the core speed estimated by the auxiliary airborne model as the feedback value. The error between the reference value and the feedback value is used as the input, and the input parameter measurement value is used as the feedforward quantity to calculate the deviation of the input parameter measurement value as the estimated deviation of the input parameter measurement value; A sensor fault mutual discrimination module, which is used to jointly perform the fault mutual discrimination of the fan speed sensor and the core speed sensor according to the relative errors of the three parameters of the fan speed, core speed and static pressure at the outlet of the high-pressure compressor of the auxiliary airborne model and the real engine. According to the sensor fault signal output by the fault mutual discrimination result, select the deviation of the input parameter measurement value calculated by the non-linear fan speed controller or the non-linear core speed controller to correct the input parameter measurement value; A model autonomous fault-tolerant correction module, which is used to realize the autonomous fault-tolerant correction of the input parameters of the airborne adaptive model and the auxiliary airborne model in the case of a fault in the fan speed sensor or the core speed sensor according to the corrected input parameter measurement value.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it realizes the steps of the method for autonomous fault-tolerant correction of the input parameters of the airborne adaptive model of an aeroengine according to any one of claims 1 to 7.

10. A storage medium, which includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the steps of the method for autonomous fault-tolerant correction of the input parameters of the airborne adaptive model of an aeroengine according to any one of claims 1 to 7.

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