Fault diagnosis method for aero fuel control pump test equipment based on parameter scheduling robust filter

Abstract

The application particularly relates to a kind of aviation fuel regulating pump test equipment fault diagnosis methods based on parameter scheduling robust filter, by establishing linear variable parameter dynamic model, parameter scheduling robust filter is constructed, while guaranteeing system pole stable distribution, the dynamic balance of fault sensitivity and interference suppression is realized;Secondly, the theory of polytope LPV system is combined with convex optimization technology, the filter gain parameter matrix of global optimization is solved through finite-dimensional linear matrix inequality set, finally, the diagnostic framework with strong robustness is formed, and the fault recognition rate and isolation response speed under complex working conditions are improved.The application can effectively solve the diagnosis robustness of traditional method under parameter perturbation and system fault, and ensure the reliable operation of fuel regulating pump test equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault diagnosis of aviation fuel regulating pump test equipment, and particularly relates to a fault diagnosis method of aviation fuel regulating pump test equipment based on a parameter scheduling robust filter. BACKGROUND

[0002] In the related art, the aviation fuel regulating pump test equipment is a precision equipment for testing the performance of a fuel regulator and a fuel pump, is a key component of an aviation engine oil supply, and the performance of the aviation engine as a whole is affected by the fuel regulating pump. The equipment realizes full-function testing of the fuel regulator and the fuel pump by integrating various advanced technologies, including servo motor dragging, test loop switching, a PLC (programmable logic controller) control system, safety protection settings, and a displacement servo control system, to realize accurate control of flow parameters. However, since the fuel regulating pump test equipment is a complex thermal coupling system working under high pressure and high speed, the safety requirement is very high. Once a fault occurs in the equipment, it will have a significant impact on the operation stability. Therefore, in order to improve the safety and reliability of the equipment, effective measures must be taken to accurately diagnose and isolate the fuel regulating pump test equipment to ensure normal operation of the equipment.

[0003] In the prior art, the fault diagnosis method for the aviation fuel regulating pump test equipment can be divided into two categories: a model-based fault diagnosis method and a data-driven fault diagnosis method. The data-driven method starts from historical data of the system, does not need to establish an analytical model, and shows directness and effectiveness in statistical analysis and information extraction of massive and multi-source data. However, the data-driven method is still in the development stage due to lack of explainability and difficulty in hardware application. In addition, the existing model-based fault diagnosis method generally uses fixed parameters for diagnosis and does not consider the influence of model uncertainty, which makes it difficult to ensure the effect of global diagnosis and affects the diagnosis performance.

[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application provides a fault diagnosis method of aviation fuel regulating pump test equipment based on a parameter scheduling robust filter, a storage medium, a computer program product, and an electronic device, to solve the sensor and actuator fault hidden danger problem of the aviation fuel regulating pump test equipment under multiple working conditions in the prior art, construct a dynamic fault diagnosis system, and thus overcome the defects in the prior art to some extent.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, or can be learned by practice of the application.

[0007] According to a first aspect of the present application, there is provided a method for diagnosing faults of an aero fuel control pump test equipment based on parameter scheduling robust filter, the method comprising:

[0008] constructing a first linear variable parameter model of the aero fuel control pump test equipment based on operation data of the aero fuel control pump test equipment, for describing dynamic characteristics of the aero fuel control pump test equipment under different working conditions, wherein the operation data comprises corresponding system rotating speed, outlet pressure and flow under different fuel metering valve opening degrees;

[0009] converting the first linear variable parameter model into a polytopic linear variable parameter model;

[0010] constructing a second linear variable parameter model based on the polytopic linear variable parameter model, combined with features of actuator faults, sensor faults, external disturbances and model uncertainties;

[0011] constructing a parameter scheduling robust filter based on a regional pole placement method, combined with the second linear variable parameter model and fault detection filter gain;

[0012] constructing a corresponding residual function based on the parameter scheduling robust filter, for judging whether there is a fault or not.

[0013] In some exemplary embodiments, the constructing a first linear variable parameter model of the aero fuel control pump test equipment based on operation data of the aero fuel control pump test equipment comprises:

[0014] selecting N fuel metering valve opening degree values at equal intervals between a fully closed state and a fully open state of the fuel metering valve, collecting corresponding system rotating speed, outlet pressure and flow data at a preset sampling frequency, to obtain a plurality of groups of operation data;

[0015] configuring the fuel metering valve opening degree FMV as an input variable u(t); configuring the system rotating speed n, the outlet pressure P out and the flow W as output variables y(t); and defining the system rotating speed n as a state variable x(t), and configuring a scheduling parameter ρ(t);

[0016] identifying each group of operation data respectively, fitting model parameters, to obtain N groups of linear state space models S j ={A j ,B j ,C}, represented as:

[0017]

[0018] Wherein, x(t) is a state variable, u(t) is an input variable, y(t) is an output variable; A j ,B j ,Crespectively are a state transition matrix, a control input matrix and an observation matrix of the system, and are constant matrices.

[0019] Solve the coefficient matrix for N groups of linear state space models and corresponding scheduling parameters, and construct an affine linear parameter-varying model of the aviation fuel regulating pump test equipment based on the coefficient matrix to describe the dynamic characteristics of the system under different working conditions, and the model is represented as:

[0020]

[0021] Wherein, ρ(t) is a scheduling parameter; A(ρ(t)), B(ρ(t)) respectively are coefficient matrices; the solution of the coefficient matrix can be based on the system speed and the polytope vertex scheduling matrix.

[0022] In some example embodiments, converting the first linear parameter-varying model into a polytope linear parameter-varying model comprises:

[0023] Converting the coefficient matrices A(ρ(t)), B(ρ(t)) respectively to obtain the converted coefficient matrices:

[0024]

[0025] Wherein, respectively are polytope vertex matrices of the polytope linear parameter-varying model; the current system matrix is determined based on the generalized distance between the current speed and the polytope vertex matrix;

[0026] Converting the first linear parameter-varying model into a polytope linear parameter-varying model based on the converted coefficient matrices.

[0027] In some example embodiments, the second linear parameter-varying model is constructed based on the polytope linear parameter-varying model, combined with the actuator failure, sensor failure, external disturbance and model uncertainty characteristics, comprising:

[0028] Define the bounded functions corresponding to the actuator failure, sensor failure and system uncertainty;

[0029] Construct the second linear parameter-varying model combined with the first linear model, the parameter uncertainty matrix of the system and each bounded function, represented as:

[0030]

[0031] Wherein, f a ,f srespectively represent the actuator failure and sensor failure; M(ρ(t)) = B(ρ(t)), N = I, I is a unit matrix, D and ε are respectively a disturbance distribution matrix and a bounded disturbance; ΔA and ΔB are respectively parameter uncertainty matrices of the system; A(ρ(t)) and B(ρ(t)) are respectively coefficient matrices in a polytopic form.

[0032] In some example embodiments, the method for configuring a region pole, in combination with a second linear variable parameter model and a fault detection filter gain, to construct a parameter scheduling robust filter comprises:

[0033]

[0034] wherein, is an estimated value of a state vector x(t), is an estimated value of an output vector y ob (t), and K(ρ(t)) is a fault detection filter gain to be solved.

[0035] In some example embodiments, the method for constructing a corresponding residual function according to the parameter scheduling robust filter comprises:

[0036]

[0037] wherein, J RMS is a residual function; r(t) is an output signal estimation error, y ob (t) is an actual measured value of an output of an aero fuel pump test device, is an estimated value of an output of a robust filter; and t is a signal length.

[0038] In some example embodiments, the method for constructing a corresponding residual function according to the parameter scheduling robust filter for judging whether a fault exists comprises:

[0039] processing the output signal estimation error by using the residual function to obtain a fault judgment parameter;

[0040] comparing the fault judgment parameter with a preset diagnosis threshold value to identify, according to a parameter comparison result, whether a fault currently exists;

[0041] if the fault judgment parameter is greater than the preset diagnosis threshold value, it is determined that a fault exists; and if the fault judgment parameter is less than or equal to the preset diagnosis threshold value, it is determined that a fault does not exist.

[0042] In some example embodiments, the method further comprises:

[0043] A corresponding special filter is configured for each sensor and actuator, and the special filter is used to monitor the corresponding sensor fault mode and / or actuator fault mode and is represented by a corresponding component in the residual vector;

[0044] When the fault judgment parameter of the residual function processing is greater than the preset diagnosis threshold, the residual signal of the first special filter / the first actuator is less than the threshold, and the residual signals of other special filters are greater than the threshold, it is determined that the first sensor / the first actuator has a fault, and fault isolation is performed.

[0045] In some example embodiments, the method further comprises:

[0046] Current operation data of the aviation fuel regulating pump test equipment is collected;

[0047] The current operation data is processed by using the parameter scheduling robust filter to obtain estimated data output by the robust filter;

[0048] The actual output signal of the aviation fuel regulating pump test equipment is compared with the estimated data to calculate an output signal estimation error;

[0049] The output signal estimation error is processed by using a residual function to obtain a current fault judgment parameter;

[0050] The current fault judgment parameter is compared with a preset diagnosis threshold to identify whether a fault exists at present according to a parameter comparison result;

[0051] When it is determined that a fault exists at present, each fault isolator is activated to determine a corresponding fault mechanism or sensor and perform fault isolation.

[0052] According to a second aspect of the present application, a computer program product is provided, which stores a computer program, and the computer program is executed by a processor to implement the aviation fuel regulating pump test equipment fault diagnosis method based on the parameter scheduling robust filter.

[0053] According to a third aspect of the present application, a storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the aviation fuel regulating pump test equipment fault diagnosis method based on the parameter scheduling robust filter.

[0054] According to a fourth aspect of the present application, an electronic device is provided, which comprises:

[0055] a processor; and

[0056] a memory for storing executable instructions of the processor;

[0057] The processor is configured to implement the above-mentioned aviation fuel regulating pump test equipment fault diagnosis method based on a parameter scheduling robust filter through executing the executable instructions.

[0058] The aviation fuel regulating pump test equipment fault diagnosis method based on a parameter scheduling robust filter provided by the embodiment of the application establishes an aviation fuel regulating pump test equipment linear parameter-varying (LPV) model considering the occurrence of sensor and actuator faults, linearly parameterizes the sensor and actuator faults and model parameter perturbation, establishes a mixed uncertainty description structure with unified representation of multiple source disturbances, and optimizes the parameter scheduling mode of the system matrix by introducing a generalized distance; for the actuator fault and sensor fault conditions, the influence of the disturbances and model uncertainties existing in the aviation fuel regulating pump test equipment LPV system is considered, the H∞ index and regional pole placement technology are fused, the fault sensitivity function and disturbance suppression ratio are optimized, and the signal attenuation rate is improved; the filter gain solving is converted into a finite-dimensional convex optimization problem, a set of linear matrix inequality constraints based on a common Lyapunov function is constructed for the vertex-dependent characteristics of the polytopic LPV system, a globally consistent filter gain parameter matrix is solved, and the stability and robustness of the system under the full envelope working condition are ensured; a fault diagnosis strategy based on residual signals is designed to realize fault detection and isolation.

[0059] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the application. BRIEF DESCRIPTION OF DRAWINGS

[0060] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application. It is apparent that the accompanying drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0061] Figure 1 The schematic diagram of the aviation fuel regulating pump test equipment fault diagnosis method based on a parameter scheduling robust filter according to an exemplary embodiment of the application is schematically shown;

[0062] Figure 2 The schematic diagram of an LPV model according to an exemplary embodiment of the application is schematically shown;

[0063] Figure 3 The schematic diagram of a gain scheduling robust filter structure according to an exemplary embodiment of the application is schematically shown;

[0064] Figure 4This diagram schematically illustrates the results of an LPV model according to an exemplary embodiment of the present invention.

[0065] Figure 5 This diagram illustrates a fault detection result based on a gain-scheduled robust filter, an exemplary embodiment of the present invention.

[0066] Figure 6 This diagram schematically illustrates a sensor fault isolation result according to an exemplary embodiment of the present invention.

[0067] Figure 7 This diagram schematically illustrates an actuator fault isolation result according to an exemplary embodiment of the present invention.

[0068] Figure 8 The diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0069] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0070] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0071] To solve the problems of sensor and actuator faults of the aviation fuel control pump test equipment under multiple working conditions in the prior art, a parameter scheduling robust filter-based aviation fuel control pump test equipment fault diagnosis method is provided in the example embodiment to construct a dynamic fault diagnosis system. By establishing a linear variable parameter dynamic model, a parameter scheduling robust filter is constructed to achieve dynamic balance of fault sensitivity and disturbance suppression while ensuring stable distribution of system poles. Secondly, the polytopic LPV system theory is combined with convex optimization technology to solve the filter gain parameter matrix of global optimization through a finite-dimensional linear matrix inequality set, and finally a diagnostic framework with strong robustness is formed to improve the fault recognition rate and isolation response speed under complex working conditions. The method can effectively solve the diagnostic robustness of traditional methods under parameter perturbation and system faults, and ensure the reliable operation of the fuel control pump test equipment.

[0072] In the example embodiment, as shown in FIG. 1, the parameter scheduling robust filter-based aviation fuel control pump test equipment fault diagnosis method can include the following steps: Figure 1

[0073] Step S11, using the operation data of the aviation fuel control pump test equipment, a first linear variable parameter model of the aviation fuel control pump test equipment is constructed to describe the dynamic characteristics of the aviation fuel control pump test equipment under different working conditions; wherein the operation data includes corresponding system speed, outlet pressure and flow under different fuel metering valve opening degrees;

[0074] Step S12, the first linear variable parameter model is converted into a polytopic linear variable parameter model;

[0075] Step S13, based on the polytopic linear variable parameter model, a second linear variable parameter model is constructed by combining the actuator fault, sensor fault, external disturbance and model uncertainty characteristics;

[0076] Step S14, based on the regional pole placement method, a parameter scheduling robust filter is constructed by combining the second linear variable parameter model and the fault detection filter gain;

[0077] Step S15, a corresponding residual function is constructed according to the parameter scheduling robust filter to judge whether there is a fault.

[0078] ​The application provides a fault diagnosis method for an aviation fuel regulating pump test device.

[0079] Next, each step of the evaluation method of the digital integrated circuit power supply network in a strong magnetic field pulse environment in the example embodiment will be described in more detail in combination with the accompanying drawings and examples.

[0080] In step S11, a first linear variable parameter model of the aviation fuel regulating pump test device is constructed using operation data of the aviation fuel regulating pump test device, and is used to describe dynamic characteristics of the aviation fuel regulating pump test device under different working conditions; wherein the operation data includes corresponding system rotation speed, outlet pressure and flow under different fuel metering valve opening degrees.

[0081] For example, step S11 can specifically include:

[0082] Step S21: selecting N fuel metering valve opening degree values at equal intervals between the fully closed state and the fully open state of the fuel metering valve, collecting corresponding system rotation speed, outlet pressure and flow data at a preset sampling frequency to obtain multiple groups of operation data;

[0083] Step S22: configuring the fuel metering valve opening degree FMV as an input variable u(t); configuring the system rotation speed n, the outlet pressure P out and the flow W as output variables y(t); and defining the system rotation speed n as a state variable x(t) and configuring it as a scheduling parameter;

[0084] Step S23: identifying each group of operation data respectively, fitting the model parameters, and obtaining N groups of linear state space models S j ={A j ,B j ,C};

[0085] Step S24: solving the coefficient matrix of the N groups of linear state space models and the corresponding scheduling parameters, and constructing an affine linear variable parameter model of the aviation fuel regulating pump test device based on the coefficient matrix to describe the dynamic characteristics of the system under different working conditions.

[0086] Specifically, from the fully closed state (0% opening) to the fully open state (100% opening) of the fuel metering valve, N fuel metering valve opening values FMV are selected at equal intervals (e.g., 2.5%) j , j = 1, 2, …, N. Different fuel metering valve opening values correspond to different fuel flow rates. Data are collected at a certain sampling frequency to obtain the operating data of the aviation fuel regulating pump test equipment under different fuel flow rates, a total of N groups of data, including fuel metering valve opening FMV j , system speed n j , outlet pressure P out,j , and flow rate W j .

[0087] For the N groups of aviation fuel regulating pump test equipment operating data, the fuel metering valve opening FMV is selected as the input variable u(t), the system speed n, the outlet pressure P out , and the flow rate W are selected as the output variable y(t), and the system speed n is selected as the scheduling parameter p. For each group of data, the least squares method is used to identify the data and fit the model parameters to obtain a group of linear state space models S j = {A j , B j , C}: represented as:

[0088]

[0089] where x(t) is the state variable, u(t) is the input variable, y(t) is the output variable; A j , B j , C are the state transition matrix, control input matrix and observation matrix of the system, respectively, and are constant matrices of a certain dimension. Finally, N groups of linear state space models are obtained, which is a linear time-invariant system to characterize the local dynamic characteristics of the aviation fuel regulating pump test equipment.

[0090] Based on the N groups of known linear state space models and the corresponding known scheduling variables, an affine linear variable parameter model of the aviation fuel regulating pump test equipment is constructed by solving the coefficient matrix through linear equations using the interpolation fitting idea, so as to better describe the dynamic characteristics of the system under different working conditions. The model is represented as:

[0091]

[0092] where p(t) is a scheduling parameter, which is measurable at any time and within a bounded set of admissible parameter trajectories. The matrices A(p(t)), B(p(t)) are related to the scheduling variable p(t) and have an affine polynomial form, i.e. A(p(t)) = A0 + p(t)A1 + p(t)A2, B(p(t)) = B0 + p(t)B1 + p(t)B2. 2 2

[0093] The coefficient matrices A0, A1, A2, B0, B1, B2 of the affine polynomial can be obtained from the following linear equations based on N sets of known linear state-space models S = {S1, S2,..., SN} and corresponding known scheduling variables p(t) = {n1, n2,..., nN}: N N

[0094]

[0095] In step 12, the first linearly parameter-varying model is converted into a polytopic linearly parameter-varying model.

[0096] For example, step S12 can specifically include: converting the coefficient matrices A(p(t)), B(p(t)) respectively, obtaining the converted coefficient matrices, and converting the first linearly parameter-varying model into a polytopic linearly parameter-varying model based on the converted coefficient matrices.

[0097] Specifically, the coefficient matrices A(p(t)), B(p(t)) can be converted respectively to obtain the converted coefficient matrices:

[0098]

[0099] where is a polytopic vertex matrix of the polytopic linearly parameter-varying model.

[0100] The coefficient matrices A0, A1, A2, B0, B1, B2 of the affine model are obtained from the following formula:

[0101]

[0102] where a1, a2 are weight functions satisfying a1 + a2 = 1.

[0103]

[0104] where ω i (i = 1, 2) represents the reciprocal of the generalized distance δ i between the current system matrix and the i-th polytopic vertex matrix, and is represented as:

[0105] ​​​​

[0106] For example, the system matrix A now For example, according to the system speed n now And the polytope vertex scheduling matrix That is, A now .

[0107] Specifically, first calculate the generalized distance between A now and The formula can include:

[0108]

[0109] Where Q is an arbitrary matrix in Hilbert space, (M1, N1) and (M2, N2) are the canonical right coprime decomposition of A now and .

[0110] Then, the generalized distance between A now and The generalized distance between A now and is calculated. Then:

[0111]

[0112] In step S13, a second linear variable parameter model is constructed based on the polyhedral linear variable parameter model, combined with the actuator failure, sensor failure, external disturbance and model uncertainty characteristics.

[0113] For example, the above step S13 can specifically include:

[0114] Step S31, define the bounded function corresponding to the actuator failure, sensor failure, system uncertainty;

[0115] Step S32, combine the first linear model, the parameter uncertainty matrix of the system and each bounded function to construct a second linear variable parameter model.

[0116] Specifically, a linear variable parameter model with actuator failure, sensor failure, external disturbance and model uncertainty is established, represented as:

[0117]

[0118] Where A(ρ(t)), B(ρ(t)) have a polyhedral form; f a , f s respectively represent the actuator failure, sensor failure. The actuator failure, sensor failure and system uncertainty are all bounded functions, i.e. ||f a ||≤λ a , ||fs ||≤λ s ,||ε(t)||≤λ ε , where λ a , λ s , λ ε are unknown positive constants. M(ρ(t)) = B(ρ(t)); N = I, I is a unit matrix, D and ε are interference distribution matrix and bounded interference respectively. ΔA, ΔB are parameter uncertainty matrices of the system, which have the following form:

[0119] ΔA = E A Σ A (t)F A , ΔB = E B Σ B (t)F B

[0120] where E A , F A , E B , F B are known real constant matrices of appropriate dimensions, M(ρ(t)) = B(ρ(t)), N = I, ∑ A (t), ∑ B (t) are unknown time-varying matrices and satisfy ∈ A T (t)∑ A (t)≤I, ∑ B T (t)∑ B (t)≤I.

[0121] And the system satisfies the following assumptions:

[0122] Assumption 1: Observable;

[0123] Assumption 2: (CF) has full row rank;

[0124] Assumption 3: For any s ∈ C, Row full rank.

[0125] In step S14, a parameter scheduling robust filter is constructed based on the region pole placement method, combined with the second linear variable parameter model and the fault detection filter gain.

[0126] Illustratively, the parameter scheduling robust filter is constructed to ensure the robustness, fault sensitivity and decay rate of the system.

[0127] Specifically, the parameter scheduling robust filter is constructed, including:

[0128]

[0129] in, It is an estimate of the state vector x(t). It is the output vector y ob The estimated value of (t), and K(ρ(t)) is the gain of the fault detection filter to be determined.

[0130] The gain of the fault detection filter to be determined has the following form:

[0131] K(ρ(t))=α1K1+α2K2

[0132] All ρ(t) below are abbreviated as ρ.

[0133] Specifically, 1) For a given γ>0, if there exists a symmetric positive definite matrix P>0 and a scalar ε i If all values ​​of ρ > 0, i = 1, 2, such that the following equation holds for all scheduling variables ρ, then the robust filter is asymptotically stable and satisfies H ∞ Performance metrics, namely |G1| ∞ <γ.

[0134]

[0135] in,

[0136] ψ1=PA(ρ)-PK(ρ)C+A T (ρ)PC T K T (ρ)P+C T C

[0137] ψ2=PD-PKF+C T F

[0138] G1 = C(sI - A) a1 D+F

[0139] 2) For a given β>0, if there exist a symmetric positive definite matrix Q>0 and a scalar ε3>0 such that for all scheduling variables ρ, the following equation holds, then the robust filter is asymptotically stable and satisfies H - Performance metrics, namely |G2| - >β.

[0140]

[0141] Among them, ∏ 11 =(A(ρ)-K(ρ)C)+(A(ρ)-K(ρ)C) T Q, G2(s) = C(sI-A) -1 M+N.

[0142] 3) Introduce the concept of regional pole placement to ensure that the filter has a certain attenuation rate. For a given pole placement region, if there exists a symmetric positive definite matrix L>0 such that the following equation holds for all scheduling variables ρ, then the robust filter is asymptotically stable and has a given pole attenuation rate.

[0143]

[0144] Where q is the center of the pole placement region and r is the diameter of the pole placement region.

[0145] 4) K(ρ(t)) can be solved by the following matrix inequalities to satisfy ‖G1‖ ∞ <γ,‖G2‖ - >β, and has a given pole decay rate.

[0146] The problem is as follows: Given γ>0, β>0, find a gain matrix K(ρ) such that the estimation error is dynamically stable and the following objective function is minimized:

[0147]

[0148] Where a1 is the weighting coefficient.

[0149] Specifically, refer to Figure 2 , Figure 4 As shown, a linear variable parameter model of an aviation fuel control pump test equipment is used. Considering the model uncertainty conditions, an LPV robust filter with H_∞ / H_- performance and pole configuration is designed to ensure the robustness of fault diagnosis to model uncertainty, fault sensitivity, and given attenuation rate. The LPV robust filter is extended to multi-cell LPV systems. The solution of the filter gain matrix is ​​transformed into solving a system of finite-dimensional linear inequalities to obtain a filter gain matrix that can meet global performance requirements. Fault detection and isolation schemes are designed to achieve fault diagnosis.

[0150] For example, constructing the corresponding residual function based on the parameter-scheduled robust filter includes:

[0151]

[0152] Among them, J RMS Let r(t) be the residual function; r(t) be the output signal estimation error; and y be the residual function. ob (t) represents the actual measured value output by the aviation fuel control pump test equipment. t represents the output estimate of the robust filter; t is the signal length.

[0153] For example, the step of constructing a corresponding residual function based on the parameter-scheduled robust filter for determining the presence of a fault includes:

[0154] The residual function processes an output signal estimation error to obtain a fault judgment parameter;

[0155] The fault judgment parameter is compared with a preset diagnosis threshold value to identify whether a fault exists according to a parameter comparison result;

[0156] If the fault judgment parameter is greater than the preset diagnosis threshold value, it is determined that a fault exists; if the fault judgment parameter is less than or equal to the preset diagnosis threshold value, it is determined that no fault exists.

[0157] Specifically, a fault detection logic is designed, and a given threshold value J th is used to make a diagnosis decision on a fault as follows:

[0158]

[0159] If the residual function is greater than the threshold value, it indicates that a system fault occurs, and fault isolation is started. A sensor fault isolation logic is designed: a dedicated filter is designed for the lth(l = 1, 2,..., m) sensor fault, and the system model is rewritten as:

[0160]

[0161] wherein the identifier represents a matrix remaining after the lth column of the matrix is removed. In the sensor fault isolation, each filter monitors a certain specific sensor fault mode, and only the lth component of the residual vector is affected by the sensor fault f s , and all other components should be zero. That is, when the lth sensor fault occurs, the fault information in the coefficient matrix of the lth reduced-order filter is removed, and the residual signal is less than the threshold value, while the residual signals of other reduced-order filters are greater than the threshold value. That is, when all sensors exceed the threshold value, actuator fault isolation is started.

[0162] For example, the method further comprises: configuring a corresponding dedicated filter for each sensor and actuator, and the dedicated filter is used to monitor a corresponding sensor fault mode and / or actuator fault mode, and is represented by a corresponding component in the residual vector;

[0163] When the fault judgment parameter obtained by the residual function processing is greater than the preset diagnosis threshold value, the residual signal of the lth dedicated filter / the ith actuator is less than the threshold value, while the residual signals of other dedicated filters are greater than the threshold value, it is determined that the lth sensor / the ith actuator has a fault, and fault isolation is performed.

[0164] Specifically, an actuator fault isolation logic is designed: a dedicated filter is designed for the ith(l = 1, 2,..., q) actuator fault, and the system model is rewritten as:

[0165]

[0166] wherein the symbol represents the matrix left after removing the i-th column. In actuator isolation, each filter monitors a specific actuator fault mode. That is, when the i-th actuator fails, the i-th reduced-order filter has the fault information removed from its coefficient matrix, and the residual signal is less than the threshold, while the residual signals of other reduced-order filters are greater than the threshold.

[0167] For example, the method further comprises:

[0168] Step S41, collecting current operation data of the aviation fuel regulating pump test equipment;

[0169] Step S42, processing the current operation data by using the parameter scheduling robust filter to obtain estimated data output by the robust filter;

[0170] Step S43, calculating output signal estimation error between the actual output signal of the aviation fuel regulating pump test equipment and the estimated data;

[0171] Step S44, processing the output signal estimation error by using a residual function to obtain a current fault judgment parameter;

[0172] Step S45, comparing the current fault judgment parameter with a preset diagnosis threshold to identify whether a fault exists at present according to a parameter comparison result;

[0173] Step S46, when it is determined that a fault exists at present, activating each fault isolator, determining a corresponding fault mechanism or sensor, and performing fault isolation.

[0174] Specifically, referring to FIG. 1, Figure 3 The fault diagnosis system can collect current operation data of the aviation fuel regulating pump test equipment in real time, including real-time data of a current throttle opening, a system speed, an outlet pressure and a flow rate. The current throttle opening value can be input into the parameter scheduling robust filter to obtain estimated values of the system speed, the outlet pressure and the flow rate output by the filter. Alternatively, the current operation data can be input into the parameter scheduling robust filter when a fault signal output by the system is detected.

[0175] Then, the collected real-time data can be compared with the estimated data to obtain output signal estimation error. The output signal estimation error is processed by using a residual function to obtain a current fault judgment parameter J RMS for the current operation data, which is compared with a threshold J thThe comparison and judgment are performed, so that whether the current exists a fault is judged.

[0176] For example, referring to FIGS. Figure 5 、 Figure 6 、 Figure 7 , the system has a fuel actuator pressure difference valve sticking fault with an amplitude of 0.12 at t=4s. When the control system is fault-free, the residual function signals fluctuate around zero. After the actuator fails, the residual function signals quickly exceed the specified threshold, and it can be concluded that the system has a fault. Since the LPV robust filter considers model parameter uncertainty, it is robust to model parameter uncertainty and external disturbances. The residual function is small in the fault-free stage, and it is larger after the fault occurs, which reflects the sensitivity of the LPV robust filter to faults. After the system detects the fault, each fault isolator is activated, and at t=4s, the residual functions of each sensor fault isolator exceed the threshold. According to the designed fault isolation logic, it indicates that an actuator fault may have occurred in the system, and at this time, the actuator fault isolator should be paid attention to. At t=4.175s, the residual function of the actuator fault isolator 2 does not exceed the threshold, and the residual functions of the other fault isolators exceed the threshold range, which can confirm that the fault corresponding to the actuator fault isolator 2 has occurred, that is, the main fuel actuator has failed. The present application can successfully isolate the actuator fault under the condition of external disturbance and model parameter uncertainty.

[0177] The method provided by the embodiment of the present application can perform more accurate fault detection and isolation by designing a parameter scheduling robust filter. First, a linear parameter-varying (LPV) model of an aviation fuel control pump test equipment considering sensor and actuator faults is established, the sensor and actuator faults and model parameter perturbation are linearly parameter-modeled, a mixed uncertainty description structure with unified representation of multiple source disturbances is established, and a parameter scheduling mode of optimizing system matrix by introducing a generalized distance is introduced; for the actuator fault and sensor fault, the influence of the disturbance and model uncertainty existing in the LPV system of the aviation fuel control pump test equipment is considered, the H∞ index and regional pole placement technology are fused, the fault sensitivity function and disturbance suppression ratio are optimized, and the signal attenuation rate is improved; the filter gain solving is converted into a finite-dimensional convex optimization problem, a linear matrix inequality constraint set based on a common Lyapunov function is constructed for the vertex-dependent characteristics of a polytope LPV system, a filter gain parameter matrix with global consistency is solved, and the stability and robustness of the system under the full envelope working condition are ensured; a fault diagnosis strategy based on a residual signal is designed, and fault detection and isolation are realized.

[0178] It is to be noted that the above-described figures are only a schematic representation of the processes comprised by the method according to the exemplary embodiments of the present application, and are not intended to limit. It is readily understood that the processes shown in the above-described figures do not indicate or limit the chronological order of these processes. In addition, it is readily understood that these processes can be executed, for example, synchronously or asynchronously in a plurality of modules.

[0179] It should be noted that, although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, according to embodiments of the present application, the features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functionalities of one module or unit described above can be further divided into embodied by a plurality of modules or units.

[0180] Figure 8 A schematic diagram of an electronic device suitable for implementing embodiments of the present application is shown.

[0181] It is to be noted that, Figure 8 The electronic device 1000 shown is only one example of an electronic device and should not be taken as limiting the scope of the functionality or use of embodiments of the present application.

[0182] As Figure 8 shown, the electronic device 1000 includes a central processing unit (CPU) 1001 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or loaded from a storage section 1008 into a random access memory (RAM) 1003. Various programs and data required for system operation are also stored in the RAM 1003. The CPU 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0183] The following components are connected to the I / O interface 1005: an input part 1006 including a keyboard, a mouse, etc.; an output part 1007 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage part 1008 including a hard disk, etc.; and a communication part 1009 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 1010 as necessary, so that a computer program read out therefrom is installed in the storage part 1008 as necessary.

[0184] In particular, according to embodiments of the present application, the processes described below with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0185] It should be noted that the storage medium shown in the embodiments of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the above two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any storage medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained in the storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination of the above.

[0186] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment, or a part of code containing one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks that are shown in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0187] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described may

[0188] It should be noted that, as another aspect, the present application also provides a storage medium, which can be included in an electronic device, or can exist independently without being assembled into the electronic device. The storage medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the method described in the embodiments. For example, the electronic device can implement each step of the method as shown in Figure 1

[0189] In one embodiment, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in the above method embodiments.

[0190] In addition, the above figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not for limiting purposes. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of the processes. In addition, it is also easy to understand that the processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0191] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the claims and their equivalents. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0192] It should be understood that the present application is not limited to the precise construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present application. The scope of the present application is limited only by the appended claims.​

Claims

1. A method for diagnosing faults of an aero fuel governing pump test equipment based on parameter scheduling robust filter, characterized in that, The method comprises: constructing a first linear variable parameter model of the aviation fuel regulating pump test equipment by using operation data of the aviation fuel regulating pump test equipment, the first linear variable parameter model being used to describe dynamic characteristics of the aviation fuel regulating pump test equipment under different working conditions, wherein the operation data comprises system rotating speed, outlet pressure and flow corresponding to different fuel metering valve opening degrees; converting the first linear variable parameter model into a polytope linear variable parameter model; constructing a second linear variable parameter model based on the polytope linear variable parameter model, in combination with the actuator failure, the sensor failure, the external disturbance and the model uncertainty, comprising: defining a bounded function corresponding to the actuator failure, the sensor failure and the system uncertainty; constructing the second linear variable parameter model in combination with the first linear model, the parameter uncertainty matrix of the system and each bounded function, and representing the second linear variable parameter model as: wherein, , respectively represent actuator faults, sensor faults; ; , is a unit matrix, and are respectively a disturbance distribution matrix and a bounded disturbance; , are respectively parameter uncertainty matrices of the system; , are respectively coefficient matrices with a polytope form; system input variables; C represents a system observation matrix; constructing a parameter scheduling robust filter based on a regional pole placement method, in combination with the second linear variable parameter model and the fault detection filter gain, comprising: wherein is an estimate of the state vector , is an estimate of the output vector , is the fault detection filter gain to be determined. constructing a corresponding residual function according to the parameter scheduling robust filter, so as to judge whether a fault exists or not, wherein the residual function comprises: wherein, is a residual function; is an output signal estimation error, is an aero fuel pump test facility output actual measurement, is a robust filter output estimate; is a signal length.

2. The method of claim 1, wherein, the first linear variable parameter model of the aviation fuel regulating pump test equipment is constructed by using operation data of the aviation fuel regulating pump test equipment, comprising: According to equal intervals, a plurality of fuel metering valve opening values are selected between a fully closed state and a fully open state of the fuel metering valve Corresponding system rotating speed, outlet pressure and flow data are collected at a preset sampling frequency to obtain a plurality of groups of operation data. Configuring fuel metering valve opening FMV as an input variable ; configuring system speed , outlet pressure , and flow as output variables ; and defining system speed as a state variable , configured as a scheduling parameter ; The running data of each group is identified respectively, model parameters are fitted, and N groups of linear state space models are obtained , which is expressed as: wherein, is a state variable, is an input variable, is an output variable; , , are a state transition matrix, a control input matrix and an observation matrix of the system, respectively, and are constant matrices. solving a coefficient matrix for N groups of linear state space models and corresponding scheduling parameters, and constructing an affine linear variable parameter model of the aviation fuel regulating pump test equipment based on the coefficient matrix, so as to describe dynamic characteristics of the system under different working conditions, and the model is represented as: wherein is a scheduling parameter; , are coefficient matrices, respectively; the solution of the coefficient matrices can be based on the system speed and the polytope vertex scheduling matrix.

3. The method of claim 2, wherein, the first linear variable parameter model is converted into a polytope linear variable parameter model, comprising: The coefficient matrix is converted as follows: , respectively to obtain the converted coefficient matrix: wherein , are polytope vertex matrices of the polytope linear varying parameter model, respectively; the current system matrix is determined based on a generalized distance between the current rotational speed and the polytope vertex matrices; the first linear variable parameter model is converted into a polytope linear variable parameter model based on the converted coefficient matrix.

4. The method of claim 1, wherein, the corresponding residual function is constructed according to the parameter scheduling robust filter, so as to judge whether a fault exists or not, comprising: output signal estimation errors are processed by using the residual function, so as to obtain a fault judgment parameter; the fault judgment parameter is compared with a preset diagnosis threshold, so as to identify whether a fault exists or not according to a parameter comparison result; if the fault judgment parameter is greater than the preset diagnosis threshold, it is determined that a fault exists; if the fault judgment parameter is less than or equal to the preset diagnosis threshold, it is determined that no fault exists.

5. The method of claim 1, wherein, The method further comprises: a corresponding special filter is configured for each sensor and actuator, the special filter being used to monitor a corresponding sensor failure mode and / or actuator failure mode, and being represented by using a corresponding component in a residual vector; When the fault determination parameter of the residual function processing is greater than the preset diagnosis threshold value, the residual signal of the first special filter / the first actuator is less than the threshold value, and the residual signal of the other special filter is greater than the threshold value, it is determined that the first sensor / the first actuator has a fault, and fault isolation is performed.

6. The method of claim 1, wherein, The method further comprises: current operation data of the aviation fuel regulating pump test equipment is collected; current operation data is processed by using the parameter scheduling robust filter, so as to obtain estimation data output by the robust filter; output signal estimation errors are calculated by comparing actual output signals of the aviation fuel regulating pump test equipment with the estimation data; current fault judgment parameters are obtained by processing the output signal estimation errors by using the residual function; the current fault judgment parameters are compared with a preset diagnosis threshold, so as to identify whether a fault exists or not according to a parameter comparison result; when it is determined that a fault exists, each fault isolator is activated, a corresponding fault actuator or sensor is determined, and fault isolation is performed.

7. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method for diagnosing faults of an aero-fuel-regulating pump test device based on a parameter-scheduled robust filter according to any one of claims 1 to 6.

8. An electronic device, comprising: Comprise: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to implement the method for diagnosing faults of an aero-fuel-regulating pump test device based on a parameter-scheduled robust filter according to any one of claims 1 to 6 via execution of the executable instructions.

9. A storage medium, characterized by A computer program product, having stored thereon a computer program, which, when executed by a processor, implements the method for diagnosing faults of an aero-fuel-regulating pump test device based on a parameter-scheduled robust filter according to any one of claims 1 to 6.

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