Aviation fuel adjusting pump test equipment fault diagnosis method based on parameter scheduling robust filter

Abstract

The invention particularly relates to an aviation fuel adjusting pump test equipment fault diagnosis method based on a parameter scheduling robust filter, and the method comprises the steps: building a linear variable parameter dynamic model, constructing the parameter scheduling robust filter, and achieving the dynamic balance of fault sensitivity and interference suppression while guaranteeing the stable distribution of system poles; and secondly, combining a multi-cell LPV system theory with a convex optimization technology, solving a global optimized filter gain parameter matrix through a finite-dimensional linear matrix inequality group, and finally forming a diagnosis architecture with high robustness, thereby improving the fault recognition rate and isolation response speed under complex working conditions. According to the method, the diagnosis robustness of a traditional method during parameter perturbation and system faults can be effectively solved, and reliable operation of fuel adjusting pump test equipment is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis of aviation fuel regulating pump test equipment, and in particular to a fault diagnosis method of aviation fuel regulating pump test equipment based on parameter scheduling robust filter. Background Art

[0002] In related technologies, aviation fuel conditioning pump test equipment is a precision device used for testing the performance of fuel regulators and fuel pumps. It is a key component of aircraft engine fuel supply, and the quality of the fuel conditioning pump affects the overall performance of aircraft engines. By integrating multiple advanced technologies, this equipment enables full-function testing of fuel regulators and fuel pumps, including servo motor drive, test circuit switching, PLC (Programmable Logic Controller) control system, safety protection settings, and displacement servo control system, enabling precise control of flow parameters. However, because the fuel conditioning pump test equipment is a complex thermodynamically coupled system operating under high pressure and high speed conditions, it has very high safety requirements. Any equipment failure will have a significant impact on operational stability. Therefore, to improve the safety and reliability of the equipment, effective measures must be taken to accurately diagnose and isolate the fuel conditioning pump test equipment to ensure its normal operation.

[0003] In the existing technology, fault diagnosis methods for aviation fuel conditioning pump test equipment can be divided into two categories: model-based and data-driven. Data-driven methods, which start from historical system data and do not require analytical models, demonstrate the directness and effectiveness of statistical analysis and information extraction from massive, multi-source data. However, data-driven methods are still in the development stage due to their lack of interpretability and the difficulty of hardware application. In addition, existing model-based fault diagnosis methods generally use fixed parameters for diagnosis and do not consider the impact of model uncertainty, making it difficult to ensure the effectiveness of global diagnosis, which in turn affects diagnostic performance.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0005] The present invention provides a fault diagnosis method for aviation fuel regulating pump test equipment based on a parameter-scheduled robust filter, a storage medium, a computer program product, and an electronic device, so as to solve the potential problems of sensor and actuator failure faced by 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 existing in the prior art to a certain extent.

[0006] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0007] According to a first aspect of the present invention, there is provided a method for diagnosing faults in an aviation fuel conditioning pump test equipment based on a parameter-scheduled robust filter, the method comprising:

[0008] Using operating data from an aviation fuel conditioning pump test device, a first linear variable parameter model of the aviation fuel conditioning pump test device is constructed to describe the dynamic characteristics of the aviation fuel conditioning pump test device under different operating conditions; wherein the operating data includes system speed, outlet pressure, and flow rate corresponding to different fuel metering valve openings;

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

[0010] Based on the polytopic linear variable parameter model, a second linear variable parameter model is constructed by combining the actuator failure, sensor failure, external interference and model uncertainty characteristics;

[0011] 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.

[0012] The corresponding residual function is constructed according to the parameter scheduling robust filter to determine whether there is a fault.

[0013] In some exemplary embodiments, constructing a first linear variable parameter mathematical model of the aviation fuel adjustment pump test equipment using operating data of the aviation fuel adjustment pump test equipment includes:

[0014] For the fuel metering valve between the fully closed state and the fully open state, N fuel metering valve opening values ​​are selected at equal intervals, and the corresponding system speed, outlet pressure and flow rate data are collected at a preset sampling frequency to obtain multiple sets of operating data;

[0015] Configure the fuel metering valve opening FMV as the input variable u(t); configure the system speed n, outlet pressure P out and flow rate W as output variable y(t); define system speed n as state variable x(t), and configure it as scheduling parameter ρ(t);

[0016] Identify each set of operating data and fit the model parameters to obtain N sets of linear state space models S j ={A j ,B j ,C}, expressed as:

[0017]

[0018] Among them, x(t) is the state variable, u(t) is the input variable, and y(t) is the output variable; A j ,B j ,C are the state transfer matrix, control input matrix and observation matrix of the system respectively, which is a constant matrix;

[0019] The coefficient matrix is ​​solved for N groups of linear state space models and the corresponding scheduling parameters. Based on the coefficient matrix, an affine linear variable parameter model of the aviation fuel pump test equipment is constructed to describe the dynamic characteristics of the system under different working conditions. The model is expressed as:

[0020]

[0021] Among them, ρ(t) is the scheduling parameter; A(ρ(t)) and B(ρ(t)) are coefficient matrices respectively; the coefficient matrix can be solved based on the system speed and the multi-vertex scheduling matrix.

[0022] In some exemplary embodiments, converting the first linear variable parameter model into a polytopic linear variable parameter model includes:

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

[0024]

[0025] in, are the polyhedral vertex matrices of the polyhedral linear variable parameter model respectively; the current system matrix is ​​determined based on the generalized distance between the current speed and the polyhedral vertex matrix;

[0026] The first linear variable parameter model is converted into a polytopic linear variable parameter model based on the converted coefficient matrix.

[0027] In some exemplary embodiments, constructing a second linear variable parameter model based on a polytopic linear variable parameter model in combination with actuator failure, sensor failure, external interference, and model uncertainty characteristics includes:

[0028] Define bounded functions corresponding to actuator failures, sensor failures, and system uncertainties;

[0029] Combining the first linear model, the system's parameter uncertainty matrix and each bounded function, the second linear variable parameter model is constructed, which can be expressed as:

[0030]

[0031] Among them, f a 、f srepresent actuator failure and sensor failure respectively; M(ρ(t))=B(ρ(t)), N=I, I is the unit matrix, D and ε are the interference distribution matrix and bounded interference respectively; ΔA and ΔB are the parameter uncertainty matrices of the system respectively; A(ρ(t)) and B(ρ(t)) are coefficient matrices with polyhedral form respectively.

[0032] In some exemplary embodiments, the regional pole placement method is combined with the second linear variable parameter model and the fault detection filter gain to construct a parameter scheduling robust filter, including:

[0033]

[0034] in, is the estimated value of the state vector x(t), is the output vector y ob (t), and K(ρ(t)) is the fault detection filter gain to be determined.

[0035] In some exemplary embodiments, the constructing a corresponding residual function according to the parameter-scheduled robust filter includes:

[0036]

[0037] Among them, J RMS is the residual function; r(t) is the output signal estimation error, y ob (t) Output of actual measured value for the aviation fuel conditioning pump test equipment, is the estimated value of robust filter output; t is the signal length.

[0038] In some exemplary embodiments, scheduling the robust filter according to the parameters to construct a corresponding residual function for determining whether a fault exists includes:

[0039] The residual function is used to process the output signal estimation error to obtain the fault judgment parameters;

[0040] Comparing the fault judgment parameters with the preset diagnostic thresholds to identify whether a fault currently exists based on the parameter comparison results;

[0041] If the fault judgment parameter is greater than the preset diagnostic threshold, it is determined that a fault exists; if the fault judgment parameter is less than or equal to the preset diagnostic threshold, it is determined that no fault exists.

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

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

[0044] When the fault judgment parameter processed by the residual function is greater than the preset diagnostic threshold, the residual signal of the lth dedicated filter / ith actuator is less than the threshold, while the residual signals of other dedicated filters are greater than the threshold, it is determined that the lth sensor / ith actuator is faulty and the fault is isolated.

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

[0046] Collect current operating data of aviation fuel conditioning pump test equipment;

[0047] Processing the current running data using a parameter-scheduled robust filter to obtain estimated data output by the robust filter;

[0048] The output signal estimation error is calculated by comparing the actual output signal of the air-fuel adjustment pump test equipment with the estimated data;

[0049] The residual function is used to process the output signal estimation error to obtain the current fault judgment parameters;

[0050] Compare the current fault judgment parameters with the preset diagnostic threshold to identify whether there is a fault based on the parameter comparison results;

[0051] When it is determined that a fault currently exists, each fault isolator is activated, the corresponding fault mechanism or sensor is determined, and the fault is isolated.

[0052] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned aviation fuel conditioning pump test equipment fault diagnosis method based on parameter scheduling robust filter is implemented.

[0053] According to a third aspect of the present invention, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned aviation fuel adjustment pump test equipment fault diagnosis method based on parameter scheduling robust filter is implemented.

[0054] According to a fourth aspect of the present invention, there is provided an electronic device, comprising:

[0055] processor; and

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

[0057] Wherein, the processor is configured to implement the above-mentioned aviation fuel adjustment pump test equipment fault diagnosis method based on parameter scheduling robust filter by executing the executable instructions.

[0058] An embodiment of the present invention provides a fault diagnosis method for aviation fuel pump test equipment based on a parameter-scheduled robust filter. This method establishes a linear parameter-varying (LPV) model of the aviation fuel pump test equipment considering sensor and actuator failures, linearly models sensor and actuator failures with model parameter perturbations, establishes a hybrid uncertainty description structure with unified representation of multi-source interference, and introduces a parameter scheduling method for the system matrix using generalized distance optimization. The method addresses actuator and sensor failures, considers the effects of interference and model uncertainty in the LPV system of the aviation fuel pump test equipment, integrates the H∞ index with regional pole placement technology, optimizes the fault sensitivity function and interference suppression ratio, and improves the attenuation rate of the implemented signal. The filter gain solution is converted into a finite-dimensional convex optimization problem. A linear matrix inequality constraint set based on a common Lyapunov function (Lyapunov equation) is constructed to address the vertex-dependent characteristics of the polycellular LPV system. A globally consistent filter gain parameter matrix is ​​solved to ensure the stability and robustness of the system under full-envelope operating conditions. A fault diagnosis strategy based on residual signals is designed to achieve fault detection and isolation.

[0059] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are incorporated into and constitute a part of this specification, illustrate embodiments consistent with the present invention, and together with the description, serve to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and it is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0061] Figure 1 A schematic diagram schematically illustrates a method for diagnosing a fault of an aviation fuel pump test equipment based on a parameter-scheduled robust filter according to an exemplary embodiment of the present invention;

[0062] Figure 2 A schematic diagram schematically illustrates an LPV model according to an exemplary embodiment of the present invention;

[0063] Figure 3 A schematic diagram schematically illustrates a gain-scheduled robust filter structure according to an exemplary embodiment of the present invention;

[0064] Figure 4A schematic diagram schematically illustrating an LPV model result according to an exemplary embodiment of the present invention;

[0065] Figure 5 A schematic diagram schematically illustrates a fault detection result based on a gain-scheduled robust filter according to an exemplary embodiment of the present invention;

[0066] Figure 6 A schematic diagram schematically illustrating a sensor fault isolation result according to an exemplary embodiment of the present invention;

[0067] Figure 7 A schematic diagram schematically illustrating a result of fault isolation of an actuator according to an exemplary embodiment of the present invention;

[0068] Figure 8 The figure schematically shows the composition of an electronic device in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0069] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example 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] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0071] In response to the shortcomings and deficiencies of the prior art, this example embodiment provides a fault diagnosis method for aviation fuel pump test equipment based on a parameter-scheduled robust filter to solve the potential problems of sensor and actuator failure faced by aviation fuel pump test equipment under multiple working conditions in the prior art, and to build a dynamic fault diagnosis system. By establishing a linear variable parameter dynamic model and constructing a parameter-scheduled robust filter, a dynamic balance between fault sensitivity and interference suppression is achieved while ensuring the stable distribution of system poles; secondly, the polyhedral LPV system theory is combined with convex optimization technology, and the filter gain parameter matrix of the global optimization is solved through a finite-dimensional linear matrix inequality group, ultimately forming a diagnostic architecture with strong robustness, thereby improving the fault recognition rate and isolation response speed under complex working conditions. This invention can effectively solve the diagnostic robustness of traditional methods in the face of parameter perturbations and system failures, and ensure the reliable operation of fuel pump test equipment.

[0072] In this example implementation, reference Figure 1 As shown, the fault diagnosis method for aviation fuel pump test equipment based on parameter scheduling robust filter may specifically include the following steps:

[0073] Step S11, constructing a first linear variable parameter model of the aircraft fuel conditioning pump test equipment using operating data of the aircraft fuel conditioning pump test equipment, for describing the dynamic characteristics of the aircraft fuel conditioning pump test equipment under different operating conditions; wherein the operating data includes system speed, outlet pressure, and flow rate corresponding to different fuel metering valve openings;

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

[0075] Step S13, constructing a second linear variable parameter model based on the polytopic linear variable parameter model in combination with actuator failure, sensor failure, external interference and model uncertainty characteristics;

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

[0077] Step S15: constructing a corresponding residual function based on the parameter scheduling robust filter to determine whether a fault exists.

[0078] This paper proposes a fault diagnosis method for aviation fuel conditioning pump test equipment. By introducing generalized distance, a linear variable parameter model of the aircraft fuel conditioning pump test equipment with sensor and actuator faults and model uncertainty is established. Taking model uncertainty into account, an LPV robust filter with H_∞ / H_- performance and pole placement is designed to ensure fault diagnosis robustness to model uncertainty, fault sensitivity, and a given attenuation rate. The LPV robust filter is extended to polycellular LPV systems, and the solution of the filter gain matrix is ​​converted into a set of finite-dimensional linear inequalities for solution. This yields a filter gain matrix that meets global performance requirements, allowing for the design of a fault detection and isolation scheme to achieve fault diagnosis.

[0079] Hereinafter, the steps of the method for evaluating a digital integrated circuit power supply network in a strong magnetic field pulse environment in this exemplary embodiment will be described in more detail with reference to the accompanying drawings and embodiments.

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

[0081] Exemplarily, the above step S11 may specifically include:

[0082] Step S21: For each fuel metering valve position between the fully closed state and the fully open state, N fuel metering valve opening values ​​are selected at equal intervals, and corresponding system speed, outlet pressure, and flow rate data are collected at a preset sampling frequency to obtain multiple sets of operating data.

[0083] Step S22, configure the fuel metering valve opening FMV as the input variable u(t); configure the system speed n, outlet pressure P out and flow rate W as output variable y(t); define system speed n as state variable x(t), and configure it as scheduling parameter;

[0084] Step S23: identify each set of operating data, fit the model parameters, and obtain N sets of linear state space models S j ={A j ,B j ,C};

[0085] In step S24 , the coefficient matrix is ​​solved for the N groups of linear state space models and the corresponding scheduling parameters, and an affine linear variable parameter model of the aviation fuel conditioning pump test equipment is constructed based on the coefficient matrix to describe the dynamic characteristics of the system under different working conditions.

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

[0087] For N groups of aviation fuel 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 The flow rate W is the output variable y(t), and the system speed n is the state variable x(t). Since the operating conditions of the aviation fuel pump test equipment (such as basic operating conditions, acceleration conditions, deceleration conditions, and idle conditions, etc.) are closely related to the system speed n, the system speed n is selected as the scheduling parameter ρ. For each set of data, the least squares method is used to identify the data and fit the model parameters to obtain a set of linear state space models S j ={A j ,B j ,C}: expressed as:

[0088]

[0089] Among them, x(t) is the state variable, u(t) is the input variable, and y(t) is the output variable; A j ,B j , C are the system's state transfer matrix, control input matrix, and observation matrix, respectively, and are constant matrices of a certain dimension. Finally, N sets of linear state space models are obtained. This model is a linear time-invariant system that characterizes the local dynamic characteristics of the aviation fuel conditioning pump test equipment.

[0090] Based on N sets of known linear state space models and corresponding known scheduling variables, we use interpolation fitting to solve the coefficient matrix of the linear equation system and construct an affine linear variable parameter model of the aviation fuel pump test equipment as shown in the following formula to better describe the dynamic characteristics of the system under different operating conditions. The model is expressed as:

[0091]

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

[0093] The coefficient matrices A0, A1, A2, B0, B1, B2 of the affine polynomials can be obtained based on N sets of known linear state space models S = {S1, S2, ..., S N} and the corresponding known scheduling variables ρ(t)={n1,n2,…,n N}, obtained by solving the following linear equations:

[0094]

[0095] In step 12, the first linear variable parameter model is converted into a polytopic linear variable parameter model.

[0096] Exemplarily, step S12 may specifically include: converting the coefficient matrices A(ρ(t)) and B(ρ(t)) respectively, obtaining the converted coefficient matrices, and converting the first linear variable parameter model into a polyhedral linear variable parameter model based on the converted coefficient matrices.

[0097] Specifically, the coefficient matrices A(ρ(t)) and B(ρ(t)) can be transformed respectively to obtain the transformed coefficient matrices:

[0098]

[0099] in, is the polyhedral vertex matrix of the polyhedral linear variable parameter model.

[0100] According to the coefficient matrix A0, A1, A2, B0, B1, B2 of the affine model, we can obtain it by the following formula:

[0101]

[0102] Among them, α1 and α2 are weight functions, satisfying α1+α2=1.

[0103]

[0104] Among them, ω i (i=1,2) represents the generalized distance δ between the current system matrix and the i-th polytope matrix i The reciprocal of is expressed as:

[0105]

[0106] For example, the system matrix A now For example, according to the system speed n now and the multitopic vertex scheduling matrix You can solve A now .

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

[0108]

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

[0110] Then, A can be calculated now and The generalized distance between Then we have:

[0111]

[0112] In step S13, a second linear variable parameter model is constructed based on the polytopic linear variable parameter model in combination with actuator failure, sensor failure, external interference and model uncertainty characteristics.

[0113] Exemplarily, the above step S13 may specifically include:

[0114] Step S31, defining bounded functions corresponding to actuator failure, sensor failure, and system uncertainty;

[0115] Step S32: Construct a second linear variable parameter model by combining the first linear model, the system's parameter uncertainty matrix and each bounded function.

[0116] Specifically, a linear variable parameter model with actuator failure, sensor failure, external interference and model uncertainty is established, which can be expressed as:

[0117]

[0118] Among them, A(ρ(t)), B(ρ(t)) have polytopic form; f a 、f s Represents actuator failure and sensor failure respectively. Actuator failure, sensor failure and system uncertainty are all bounded functions, that is, ||f a ||≤λ a ,||fs ||≤λ s ,||ε(t)||≤λ ε , where λ a ,λ s ,λ ε is an unknown constant greater than zero. M(ρ(t))=B(ρ(t)); N=I, where I is the identity matrix, D and ε are the interference distribution matrix and bounded interference, respectively. ΔA and ΔB are the parameter uncertainty matrices of the system, respectively, and have the following form:

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

[0120] Among them, E A ,F A ,E B ,F B are all real constant matrices of appropriate dimensions, M(ρ(t))=B(ρ(t)), N=I, ∑ A (t),∑ B (t) is an unknown time-varying matrix and satisfies ∈ A T (t)∑ A (t)≤I,∑ B T (t)∑ B (t)≤I.

[0121] Furthermore, 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, Full rank.

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

[0126] Exemplarily, a parameter-scheduled robust filter is constructed to ensure the robustness, fault sensitivity and attenuation rate of the system.

[0127] Specifically, constructing a parameter-scheduled robust filter includes:

[0128]

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

[0130] is the fault detection filter gain to be determined, which 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 >0,i=1,2, so that for all scheduling variables ρ, the following equation holds true, then the robust filter is asymptotically stable and satisfies H ∞ Performance index, 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 exists 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 index, 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) Introducing the concept of regional pole placement ensures 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 for all scheduling variables ρ, the following equation holds, 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)) is solved jointly by the following matrix inequalities, which can satisfy ‖G1‖ ∞ <γ,‖G2‖ - >β, and has a given extreme decay rate.

[0146] The problem to be solved is as follows: For a given γ>0, β>0, solve a gain matrix K(ρ) so that the estimation error is dynamically stable and the following objective function is minimized:

[0147]

[0148] Among them, a1 is the weight coefficient.

[0149] Specifically, refer to Figure 2 、 Figure 4 As shown in the figure, the linear variable parameter model of the aviation fuel regulating pump test equipment is designed. 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 a given attenuation rate; the LPV robust filter is extended to the polycellular LPV system, and the solution of the filter gain matrix is ​​converted into a finite-dimensional linear inequality group for solution to obtain the filter gain matrix that can meet the global performance requirements. The fault detection and isolation scheme is designed to realize fault diagnosis.

[0150] Exemplarily, the constructing a corresponding residual function according to the parameter scheduling robust filter includes:

[0151]

[0152] Among them, J RMS is the residual function; r(t) is the output signal estimation error, y ob (t) Output of actual measured value for the aviation fuel conditioning pump test equipment, is the estimated value of robust filter output; t is the signal length.

[0153] Exemplarily, the scheduling of the robust filter according to the parameters to construct a corresponding residual function for determining whether a fault exists includes:

[0154] The residual function is used to process the output signal estimation error to obtain the fault judgment parameters;

[0155] Comparing the fault judgment parameters with the preset diagnostic thresholds to identify whether a fault currently exists based on the parameter comparison results;

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

[0157] Specifically, the fault detection logic is designed to detect the fault according to the given threshold J th , the fault diagnosis decision can be made through the following logical relationship:

[0158]

[0159] If the residual function is greater than the threshold, it means that the system has failed and fault isolation is enabled. Design sensor fault isolation logic: Design a dedicated filter for the lth (l=1,2,…,m) sensor fault. The system model is rewritten as:

[0160]

[0161] Among them, the identifier symbol In sensor fault isolation, each filter monitors a specific sensor fault mode, and only the lth component of the residual vector is affected by the sensor fault f s , all other components should be zero. That is, when the lth sensor fault occurs, the lth reduced-order filter's coefficient matrix is ​​precisely removed from the fault information, resulting in a residual signal less than the threshold, while the residual signals of the other reduced-order filters are greater than the threshold. In other words, if all sensors exceed the threshold, actuator fault isolation is initiated.

[0162] Exemplarily, the method further includes: configuring a corresponding dedicated filter for each sensor and actuator, the dedicated filter being used to monitor the corresponding sensor failure mode and / or actuator failure mode, and representing the corresponding component in the residual vector;

[0163] When the fault judgment parameter processed by the residual function is greater than the preset diagnostic threshold, the residual signal of the lth dedicated filter / ith actuator is less than the threshold, while the residual signals of other dedicated filters are greater than the threshold, it is determined that the lth sensor / / ith actuator is faulty and the fault is isolated.

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

[0165]

[0166] Among them, the identifier symbol represents the matrix remaining after removing the i-th column from this matrix. In actuator isolation, each filter monitors a specific actuator failure mode. That is, when the i-th actuator failure occurs, the i-th reduced-order filter's coefficient matrix has the fault information removed, resulting in a residual signal less than the threshold, while the residual signals of the other reduced-order filters are greater than the threshold.

[0167] Exemplarily, the method further includes:

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

[0169] Step S42, using a parameter-scheduled robust filter to process the current running data and obtain estimated data output by the robust filter;

[0170] Step S43, calculating the output signal estimation error by comparing the actual output signal of the air-fuel adjustment pump test equipment with the estimated data;

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

[0172] Step S45, comparing the current fault judgment parameter with a preset diagnostic threshold to identify whether a fault currently exists based on the parameter comparison result;

[0173] Step S46: When it is determined that a fault currently exists, each fault isolator is activated, the corresponding fault mechanism or sensor is determined, and the fault is isolated.

[0174] Specifically, refer to Figure 3 As shown, the fault diagnosis system can collect real-time operating data from the aircraft fuel pump test equipment, including real-time data on the current throttle opening, system speed, outlet pressure, and flow rate. The current throttle opening value can be input into a parameter-scheduled robust filter to obtain outputs of estimated values ​​for the system speed, outlet pressure, and flow rate. Alternatively, the current operating data can be input into the parameter-scheduled robust filter when a system fault signal is detected.

[0175] Then, the collected real-time data can be compared with the estimated data to obtain the output signal estimation error. The output signal estimation error is processed using the residual function to obtain the current fault judgment parameter J corresponding to the current operating data. RMS and with the threshold J thA comparison is performed to determine whether a fault exists. If a fault exists, each fault isolator is activated to determine the corresponding fault mechanism or sensor and perform fault isolation.

[0176] For example, refer to Figure 5 、 Figure 6 、 Figure 7 As shown, at 4 seconds, a fuel actuator differential pressure valve stuck fault with an amplitude of 0.12 occurred in the system. When the control system was fault-free, the residual function signal fluctuated around zero. After the actuator fault occurred, the residual function signal rapidly exceeded the specified threshold, concluding that the system was faulty. Because the LPV robust filter accounts for model parameter uncertainty and is robust to both model parameter uncertainty and external interference, the residual function is small in the fault-free phase and larger after the fault occurs, demonstrating the LPV robust filter's sensitivity to faults. After the system detected the fault, the fault isolators were activated. At t = 4 seconds, the residual function of each sensor fault isolator exceeded the threshold. According to the designed fault isolation logic, this indicates that an actuator fault may have occurred in the system, and attention should be paid to the actuator fault isolator. At t = 4.175 seconds, the residual function of actuator fault isolator 2 did not exceed the threshold, while the other residual functions all exceeded the threshold range. This confirms that the fault corresponding to actuator fault isolator 2 has occurred, namely, the main fuel actuator has failed. The present invention can successfully isolate actuator faults in the presence of external interference and model parameter uncertainty.

[0177] The method provided by an embodiment of the present invention achieves more accurate fault detection and isolation by designing a parameter-scheduled robust filter. First, a linear parameter-varying (LPV) model of an aviation fuel pump test rig is established, taking into account sensor and actuator failures. The sensor and actuator failures are then linearly modeled with model parameter perturbations, resulting in a hybrid uncertainty description structure with a unified representation of multi-source interference. A parameter scheduling approach for the system matrix is ​​introduced using generalized distance optimization. For actuator and sensor failures, the effects of interference and model uncertainty in the LPV system of the aviation fuel pump test rig are considered. The H∞ index and regional pole placement techniques are integrated to optimize the fault sensitivity function and interference rejection ratio, and improve the signal attenuation rate. The filter gain solution is transformed into a finite-dimensional convex optimization problem. Taking into account the vertex-dependent characteristics of the polyhedral LPV system, a linear matrix inequality constraint set based on the common Lyapunov function is constructed to solve a globally consistent filter gain parameter matrix, ensuring the stability and robustness of the system under full-envelope operating conditions. Finally, a fault diagnosis strategy based on residual signals is designed to achieve fault detection and isolation.

[0178] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0179] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

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

[0181] It should be noted that Figure 8 The electronic device 1000 shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

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

[0183] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1008 including a hard disk and the like; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. Removable media 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1010 as needed, so that computer programs read therefrom can be installed into the storage section 1008 as needed.

[0184] In particular, according to an embodiment of the present invention, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a storage medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1009 and / or installed from a removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, the various functions defined in the system of the present application are performed.

[0185] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but 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 computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any storage medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0186] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0187] The units involved in the embodiments of the present invention may be implemented in software or hardware, and the units described may also be provided in a processor. In some cases, the names of these units do not limit the units themselves.

[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 it can exist independently without being installed in the electronic device. The above storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiments. For example, the electronic device can implement the following Figure 1 The individual steps of the method are shown.

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

[0190] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0191] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the claims.

[0192] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof, which is limited only by the appended claims.

Claims

1. A fault diagnosis method for aviation fuel pump test equipment based on parameter scheduling robust filter, characterized in that: The method comprises: Using operating data from an aviation fuel conditioning pump test device, a first linear variable parameter model of the aviation fuel conditioning pump test device is constructed to describe the dynamic characteristics of the aviation fuel conditioning pump test device under different operating conditions; wherein the operating data includes system speed, outlet pressure, and flow rate corresponding to different fuel metering valve openings; Converting the first linear variable parameter model into a polytopic linear variable parameter model; Based on the polytopic linear variable parameter model, a second linear variable parameter model is constructed by combining the actuator failure, sensor failure, external interference and model uncertainty characteristics; 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. The corresponding residual function is constructed according to the parameter scheduling robust filter to determine whether there is a fault.

2. The method according to claim 1, characterized in that The method of constructing a first linear variable parameter mathematical model of the aviation fuel regulating pump test equipment using the operating data of the aviation fuel regulating pump test equipment includes: For the fuel metering valve between the fully closed state and the fully open state, N fuel metering valve opening values ​​are selected at equal intervals, and the corresponding system speed, outlet pressure and flow rate data are collected at a preset sampling frequency to obtain multiple sets of operating data; Configure the fuel metering valve opening FMV as the input variable u(t); configure the system speed n, outlet pressure P out and flow rate W as output variable y(t); define system speed n as state variable x(t), and configure it as scheduling parameter ρ(t); Identify each set of operating data and fit the model parameters to obtain N sets of linear state space models S j ={A j ,B j ,C}, expressed as: Among them, x(t) is the state variable, u(t) is the input variable, and y(t) is the output variable; A j ,B j ,C are the state transfer matrix, control input matrix and observation matrix of the system respectively, which is a constant matrix; The coefficient matrix is ​​solved for N groups of linear state space models and the corresponding scheduling parameters. Based on the coefficient matrix, an affine linear variable parameter model of the aviation fuel pump test equipment is constructed to describe the dynamic characteristics of the system under different working conditions. The model is expressed as: Among them, ρ(t) is the scheduling parameter; A(ρ(t)) and B(ρ(t)) are coefficient matrices respectively; the coefficient matrix can be solved based on the system speed and the multi-vertex scheduling matrix.

3. The method according to claim 2, characterized in that Converting the first linear variable parameter model into a polytopic linear variable parameter model includes: Convert the coefficient matrices A(ρ(t)) and B(ρ(t)) respectively to obtain the converted coefficient matrices: in, are the polyhedral vertex matrices of the polyhedral linear variable parameter model respectively; the current system matrix is ​​determined based on the generalized distance between the current speed and the polyhedral vertex matrix; The first linear variable parameter model is converted into a polytopic linear variable parameter model based on the converted coefficient matrix.

4. The method according to claim 1, wherein The second linear variable parameter model is constructed based on the polytope linear variable parameter model and combined with the actuator failure, sensor failure, external interference and model uncertainty characteristics, including: Define bounded functions corresponding to actuator failures, sensor failures, and system uncertainties; Combining the first linear model, the system's parameter uncertainty matrix and each bounded function, the second linear variable parameter model is constructed, which can be expressed as: Among them, f a 、f s represent actuator failure and sensor failure respectively; M(ρ(t))=B(ρ(t)); N=I, where I is the unit matrix, D and ε are the interference distribution matrix and bounded interference respectively; ΔA and ΔB are the parameter uncertainty matrices of the system respectively; A(ρ(t)) and B(ρ(t)) are coefficient matrices with polyhedral form respectively.

5. The method according to claim 1, wherein The method based on regional pole placement, combined with the second linear variable parameter model and the fault detection filter gain, constructs a parameter scheduling robust filter, including: in, is the estimated value of the state vector x(t), is the output vector y ob (t), and K(ρ(t)) is the fault detection filter gain to be determined.

6. The method according to claim 1, characterized in that The method of constructing a corresponding residual function according to the parameter scheduling robust filter includes: Among them, J RMS is the residual function; r(t) is the output signal estimation error, y ob (t) Output of actual measured value for the aviation fuel conditioning pump test equipment, is the estimated value of robust filter output; t is the signal length.

7. The method according to claim 6, characterized in that The method of scheduling the robust filter according to the parameters to construct a corresponding residual function for determining whether a fault exists includes: The residual function is used to process the output signal estimation error to obtain the fault judgment parameters; Comparing the fault judgment parameters with the preset diagnostic thresholds to identify whether a fault currently exists based on the parameter comparison results; If the fault judgment parameter is greater than the preset diagnostic threshold, it is determined that a fault exists; if the fault judgment parameter is less than or equal to the preset diagnostic threshold, it is determined that no fault exists.

8. The method according to claim 1, characterized in that The method further comprises: A corresponding dedicated filter is configured for each sensor and actuator, and the dedicated filter is used to monitor the corresponding sensor failure mode and / or actuator failure mode, and is represented by the corresponding component in the residual vector; When the fault judgment parameter processed by the residual function is greater than the preset diagnostic threshold, the residual signal of the lth dedicated filter / ith actuator is less than the threshold, while the residual signals of other dedicated filters are greater than the threshold, it is determined that the lth sensor / ith actuator is faulty and the fault is isolated.

9. The method according to claim 1, characterized in that The method further comprises: Collect current operating data of aviation fuel conditioning pump test equipment; Processing the current running data using a parameter-scheduled robust filter to obtain estimated data output by the robust filter; The output signal estimation error is calculated by comparing the actual output signal of the air-fuel adjustment pump test equipment with the estimated data; The residual function is used to process the output signal estimation error to obtain the current fault judgment parameters; Compare the current fault judgment parameters with the preset diagnostic threshold to identify whether there is a fault based on the parameter comparison results; When it is determined that a fault currently exists, each fault isolator is activated, the corresponding fault mechanism or sensor is determined, and the fault is isolated.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for diagnosing faults of an aviation fuel adjustment pump test equipment based on a parameter-scheduled robust filter according to any one of claims 1 to 9 is implemented.

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