Airborne equipment phm virtual-real integration experimental platform and experimental method

Abstract

The application discloses an aviation equipment PHM virtual-real integration experiment platform and an experiment method, and belongs to the technical field of aircrafts. The platform comprises a component-level PHM physical experiment module configured to realize PHM physical experiments of important components of an airplane and collect test data, and a whole-machine-level PHM virtual simulation experiment module configured to acquire the test data of the component-level PHM physical experiment module and perform digital simulation on the whole airplane and a scene; corresponding PHM faults can be injected in the simulation process. The method comprises a physical platform experiment process, a digital simulation experiment process and a virtual-real integration experiment process. The application can solve the technical problems of single PHM research level, insufficient virtual-real combination capability and limited verification conditions in the prior art, and realizes multi-level PHM research and verification from the component level to the whole-machine level.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of aircrafts, and particularly relates to an aviation equipment PHM virtual-real integration experiment platform and an experiment method. BACKGROUND

[0002] Aircraft PHM (Prognostics and Health Management) is a comprehensive system based on multidisciplinary technology, aiming to realize early prediction of faults, health state evaluation and optimization of maintenance decision through real-time monitoring and data analysis of key components and systems of an aircraft, so as to improve flight safety, reduce operation cost and improve maintenance efficiency. Aircraft PHM is a key technology in the field of aviation operation and maintenance, and has become a standard function of modern civil aircrafts.

[0003] With the continuous improvement of the complexity of aviation equipment, the traditional single-level and single-mode PHM research method has been difficult to meet the needs of system-level health management of modern aviation equipment. The existing technology has the following problems:

[0004] 1. Research level limitation: existing laboratories are mostly engaged in component-level PHM research work (such as bearings and gearboxes), which is difficult to match the whole-machine-level PHM research paradigm and application requirements.

[0005] 2. Insufficient verification conditions: lacking of aircraft whole-machine fault simulation and PHM method verification conditions, unable to carry out system-level fault propagation and influence analysis.

[0006] 3. Strong platform closure: traditional experiment platforms are strongly closed, and it is difficult to cooperate with laboratories, colleges and research institutes of different backgrounds.

[0007] 4. Insufficient virtual-real combination capability: lacking of effective combination of virtual simulation and physical experiment, unable to realize seamless integration of real physical signals and virtual environment. SUMMARY

[0008] The present application aims to at least partially solve one of the above-mentioned technical problems in the related art.

[0009] To this end, the present application aims to provide an aviation equipment PHM virtual-real integration experiment platform and experiment method, which can solve the technical problems of single PHM research level, insufficient virtual-real combination capability and limited verification conditions in the prior art, and realize multi-level PHM research and verification from component level to whole-machine level.

[0010] In order to solve the above technical problems, the present application is implemented as follows:

[0011] The embodiment of the present application provides an aviation equipment PHM virtual-real integration experiment platform, which comprises:

[0012] a component-level PHM physical experiment module configured to implement a PHM physical experiment of an important component of an aircraft and collect test data; and

[0013] a whole-machine-level PHM virtual simulation experiment module configured to obtain the test data of the component-level PHM physical experiment module, perform digital simulation of the whole machine and a scene of the aircraft, and inject corresponding PHM faults in the simulation process.

[0014] In addition, the virtual-real integrated experiment platform for aviation equipment PHM according to the present applicationapplicationhave the following additional technical features:

[0015] In some embodiments, the component-level PHM physical experiment moduleapplicationinclude:

[0016] an inner-outer dual-rotor simulation experiment table for an aero-engine configured to implement fault simulation of rotor blade cracks, motor faults and rotor imbalance; and

[0017] a transmission system multi-fault fusion simulation experiment table configured to simulate planetary or parallel gear box faults and bearing faults.

[0018] In some embodiments, the whole-machine-level PHM virtual simulation experiment moduleapplicationinclude:

[0019] an aircraft digital simulation system configured to implement simulation of main systems of the aircraft and interactive simulation of onboard PHM;

[0020] an aircraft scene simulation test platform configured to support automatic testing, experiment script making, test process monitoring and fault injection functions; and

[0021] an aviation PHM fault injection system configured to support various fault simulation of aviation systems and implement multi-system fault characteristic simulation.

[0022] The present application also provides an aviation equipment PHM virtual-real integrated experiment method, which is implemented by using the aviation equipment PHM virtual-real integrated experiment platform according to any one of the above.

[0023] The methodapplicationinclude a physical platform experiment process, a digital simulation experiment process and a virtual-real integrated experiment process.

[0024] In addition, the aviation equipment PHM virtual-real integrated experiment method according to the present applicationapplicationhave the following additional technical features:

[0025] In some embodiments, the physical platform experiment processapplicationinclude:

[0026] S1, set a gearbox fault in the transmission system physical experiment table, and collect vibration and rotation speed data through a sensor;

[0027] S2, process the data collected in S1, extract fault features, and verify the diagnosis algorithm.

[0028] In some embodiments, the digital simulation experiment process comprises:

[0029] S1, inject a power system fault model in the aircraft digital simulation system;

[0030] S2, simulate flight conditions through a scene test platform, generate whole-machine-level fault data, and analyze the cascading influence of the fault on the fuel system and the hydraulic system.

[0031] In some embodiments, the virtual-real integrated experiment process comprises:

[0032] S1, simulate a bearing progressive degradation fault in the physical experiment table, and collect real-time vibration signals;

[0033] S2, inject the real-time vibration signals into the virtual simulation platform, drive the whole-machine digital model to deduce the influence of the fault on the flight control system;

[0034] S3, perform residual life prediction on the deduced whole-machine state data, and verify the accuracy of the algorithm.

[0035] Compared with the prior art, the present application has at least the following beneficial effects:

[0036] In the embodiment of the present application, the provided virtual-real integrated experiment platform for aviation equipment PHM has multi-level research capability: it can realize multi-granularity PHM research from the component level to the whole-machine level, and break through the limitations of traditional single-level research;

[0037] In the embodiment of the present application, the provided virtual-real integrated experiment platform for aviation equipment PHM has virtual-real deep integration capability: it can realize seamless integration of virtual simulation and physical experiment through a standard data interface, and provide comprehensive data support for AI algorithms;

[0038] In the embodiment of the present application, the provided virtual-real integrated experiment platform for aviation equipment PHM is highly open: it supports the design, integration and verification of different types of fault diagnosis, health assessment and life prediction models;

[0039] In the embodiment of the present application, the provided virtual-real integrated experiment platform for aviation equipment PHM has a wide range of applications: it can serve various needs such as PHM design and development, algorithm verification, performance evaluation and teaching training;

[0040] The aviation equipment PHM virtual-real integrated experiment platform provided in the embodiment of the present application improves scientific research capability: the whole machine level PHM research paradigm is broken through, the complex system fault propagation mechanism is supported, and experimental support is provided for aviation equipment airworthiness certification and maintenance strategy optimization;

[0041] The aviation equipment PHM virtual-real integrated experiment platform provided in the embodiment of the present application promotes teaching practice innovation: through the experiment mode of virtual-real combination, the design thinking of students at the system level is cultivated, and the gap between theory and engineering application is shortened.

[0042] The aviation equipment PHM virtual-real integrated experiment platform provided in the embodiment of the present application promotes industry collaborative development: the open platform architecture supports cross-disciplinary cooperation in the fields of aviation, aerospace, energy and the like, accelerates the transformation of PHM technology achievements, and promotes the realization of the goal of'reducing cost and increasing efficiency' of intelligent civil aviation.

[0043] The aviation equipment PHM virtual-real integrated experiment method of the present application is realized by using the aviation equipment PHM virtual-real integrated experiment platform, and thus at least has all the characteristics and advantages of the aviation equipment PHM virtual-real integrated experiment platform, which will not be repeated here. Additional aspects and advantages of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The aviation equipment PHM virtual-real integrated experiment method disclosed for an embodiment of the present application is shown in the figure;

[0045] Figure 2 The aviation equipment PHM virtual-real integrated experiment platform structure block diagram disclosed for an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0047] The embodiments of the present application will be described in detail below with reference to the drawings and specific embodiments and their application scenarios.

[0048] Please refer to Figures 1-2 As shown in the figure, in some embodiments of the present application, an aviation equipment PHM virtual-real integrated experiment platform is provided, which comprises: a whole machine level PHM virtual simulation experiment table, a component level PHM physical experiment table, a data interconnection and intercommunication system, and a data training and algorithm development platform.

[0049] In some embodiments of the present application, the whole machine level PHM virtual simulation test bench (an aircraft simulation test platform in the whole machine level PHM virtual simulation test bench) Figure 2 comprises:

[0050] an aircraft digital simulation system, configured to run aircraft main system simulation software and onboard PHM interactive simulation;

[0051] an aircraft scene simulation test platform, configured to support automatic testing, experiment script making, test process monitoring and fault injection functions;

[0052] an aviation PHM fault injection system, configured to support various fault simulation of aviation systems and realize multi-system fault characteristic simulation.

[0053] The aircraft digital simulation system adopts a multi-field coupling modeling method to construct a complete aircraft outline level simulation model, comprising:

[0054] avionics system modeling: a discrete event modeling method is adopted to construct an avionics network communication model, a data processing model and a human-computer interaction model; a fault propagation model and a redundancy switching logic model of the avionics system are established;

[0055] electromechanical system modeling: the electromechanical systems such as hydraulic system, fuel system and environmental control system are modeled based on the bond graph theory; a dynamic characteristic model of each subsystem is established by using the lumped parameter method;

[0056] power system modeling: an engine thrust characteristic model is established based on the thermodynamic cycle theory; a dynamic response of the power system is described by using a mathematical model of a turboshaft engine;

[0057] multi-body dynamics and aerodynamics coupling modeling: a rigid body six-degree-of-freedom motion equation of the aircraft is established by using the Lagrange equation; an aerodynamics model is established based on the CFD method.

[0058] Key modeling equations include: aircraft six-degree-of-freedom motion equation, engine thrust model and hydraulic system modeling equation.

[0059] The aircraft six-degree-of-freedom motion equation comprises:

[0060] center of mass motion equation:

[0061]

[0062]

[0063]

[0064] rotation equation:

[0065]

[0066]

[0067]

[0068] The engine thrust model comprises:

[0069] The thrust equation is:

[0070]

[0071] The fuel consumption rate is:

[0072]

[0073] Wherein: The rotation speed, The Mach number, The altitude, The temperature before the turbine.

[0074] The hydraulic system modeling equation comprises:

[0075] The flow continuity equation is:

[0076]

[0077] The momentum equation is:

[0078]

[0079] The actuator force balance equation is:

[0080]

[0081] In the above embodiment, the whole machine level PHM virtual simulation test bench also has a software architecture. A layered architecture design is adopted, including a physical model layer, a numerical solution layer, a simulation service layer and a user interface layer, which can support switching between real-time and non-real-time simulation modes. The main input data includes flight state data, system configuration data and sensor data.

[0082] The flight state data includes position information, motion state and environmental parameters. The system configuration data includes system initial state matrix and fault injection parameters. The sensor parameters are presented in a data packet structure.

[0083] In the above embodiment, the fault model modeling of the aviation PHM fault injection system is based on physical mechanism fault modeling, adopts a degradation process modeling method to establish a physical model of component performance degradation, and constructs a fatigue crack propagation model based on damage accumulation theory.

[0084] The fault injection algorithm, and the fault signal generation model is:

[0085]

[0086] wherein: is a multiplicative fault factor, is an additive fault factor, is a degradation function, is a fault noise.

[0087] Multi-level fault propagation model:

[0088] Fault propagation matrix: represents the probability of fault propagation from component i to component j.

[0089] System fault state evolution:

[0090] Fault mode library construction: a fault mode database covering mechanical, electrical, hydraulic, avionics and other systems is established, and a fault knowledge graph is constructed by using the fault mode and effect analysis (FMEA) method.

[0091] In some embodiments of the present application, the component-level PHM physical experiment table (a physical platform for simulating faults of core components of an aircraft in the component-level PHM system) Figure 2 ) comprises:

[0092] The inner and outer dual-rotor simulation experiment table of the aero-engine can realize simulation of rotor blade cracks, motor faults, rotor imbalance and the like.

[0093] The multi-fault fusion simulation experiment table of the transmission system can simulate planetary / parallel gear box faults, bearing faults and the like.

[0094] In the above embodiment, in terms of specific implementation of the inner and outer dual-rotor simulation experiment table of the aero-engine, the differential transmission of the inner and outer rotors is realized by using a planetary gear system, the rotation speed range of the inner rotor is 0-6000 rpm, and the rotation speed range of the outer rotor is 0-4000 rpm; the fault simulation mechanism is to realize 0.1-5.0 g mm imbalance fault by using an adjustable mass block, and to simulate a blade crack with a depth of 0.1-2.0 mm by using an electric spark machining; and the multi-parameter monitoring integrates a 16-channel synchronous acquisition system, and the sampling frequency can reach 50 kHz.

[0095] The rotor dynamics modeling includes an inner rotor motion equation, an outer rotor motion equation and a coupling torque equation.

[0096] The inner rotor motion equation is:

[0097] The outer rotor motion equation is:

[0098] The coupling torque equation is:

[0099] In the above embodiment, the transmission system multi-fault fusion simulation experiment table has multi-fault coupling capability and can simultaneously simulate up to 5 kinds of component faults such as gear box, bearing, shaft coupling, etc.; It also has fault degree quantization control, which realizes 0-2000N radial force loading through a precision servo loading device, with a loading accuracy of ±1N.

[0100] The gear box fault modeling includes:

[0101] The gear meshing stiffness time-varying model:

[0102] The fault gear vibration equation:

[0103] .

[0104] In some embodiments of the application, a data interconnection and interconnection system (such as the aviation PHM fault injection system in the figure) is used to realize data exchange and signal transmission between the virtual simulation platform and the physical experiment table. It has a data fusion and synchronization mechanism.

[0105] The time synchronization algorithm includes:

[0106] Precise clock synchronization:

[0107] Clock bias estimation:

[0108] The data fusion algorithm includes:

[0109] Weighted fusion model:

[0110] Where the weight is calculated as: , is the measurement uncertainty.

[0111] In some embodiments of the application, a data training and algorithm development platform includes a PHM supercomputing training platform and a design and algorithm development platform.

[0112] The application also provides an experimental method based on the above platform, including three experimental modes.

[0113] First experimental mode: physical platform experiment process

[0114] The experimental goal is to carry out fault and performance degradation simulation experiments of aviation core components and subsystems based on the physical experiment table, and to obtain real fault feature data. The steps include:

[0115] Step 1: Experiment table preparation and fault setting

[0116] 1.1: Setting of the aviation engine inner and outer double rotor simulation experiment table

[0117] Start the inner and outer rotor system, set the operating parameters: inner rotor speed 0-6000 rpm, outer rotor speed 0-4000 rpm.

[0118] According to the experimental requirements, set specific faults, including: blade crack fault: use electric spark machining technology to process cracks with a depth of 0.1-2.0mm on the blade; rotor imbalance fault: set an imbalance of 0.1-5.0g·mm by adjustable mass block; motor fault: set inner rotor eccentricity or inner bearing crack fault.

[0119] 1.2: Transmission system multi-fault fusion simulation experiment table setting

[0120] Configure parallel axis fault simulation gearbox and planetary gearbox;

[0121] Set up a composite fault mode, including: gearbox fault: simulate gear tooth breakage, tooth wear and other faults; bearing fault: apply a radial force of 0-2000N by a radial loading device, with an accuracy of ±1N; multi-fault coupling: set up to 5 different component faults at the same time.

[0122] Step 2: Sensor system configuration and data acquisition

[0123] 2.1: Sensor arrangement

[0124] Install a 16-channel synchronous acquisition system sensor, including: three-axis acceleration sensor: monitor X, Y, Z three direction vibration signal; eddy current sensor: measure shaft displacement; speed sensor: real-time monitor rotor speed; temperature sensor: monitor temperature changes in key positions.

[0125] 2.2: Data acquisition parameter setting

[0126] Set the sampling frequency: 50kHz (ensure complete capture of fault features);

[0127] Configure data format.

[0128] Step 3: Experiment execution and monitoring

[0129] 3.1: Experiment running

[0130] Start the physical experiment table and run according to the preset working condition;

[0131] Monitor the experiment table status in real time to ensure safe operation;

[0132] Record the changes of key parameters during the experiment.

[0133] 3.2: Real-time data acquisition

[0134] Continuous acquisition of multi-channel sensor data through data acquisition system;

[0135] Real-time display of vibration waveform and spectrum analysis results;

[0136] Record the timestamp and state information when the fault occurs.

[0137] Step 4: Data processing and feature extraction

[0138] 4.1: Signal preprocessing

[0139] Filter and denoise the collected raw signal;

[0140] Perform time domain and frequency domain feature extraction, including time domain features: mean, variance, peak value, kurtosis, skewness, etc.; frequency domain features: power spectral density, characteristic frequency amplitude, etc.

[0141] 4.2: Fault feature identification

[0142] Analyze fault characteristic frequency based on physical mechanism;

[0143] Establish a fault feature vector library;

[0144] Compare the signal differences between normal and fault states.

[0145] Step 5: Algorithm verification and optimization

[0146] 5.1: Transfer processed data to algorithm development platform

[0147] Send data to PHM algorithm design and development platform through test communication network;

[0148] Use 2 NVIDIA A100 GPUs for algorithm training and verification.

[0149] 5.2: Diagnostic algorithm verification

[0150] Verify the accuracy and robustness of the fault diagnosis algorithm;

[0151] Evaluate the performance of the algorithm under different fault severity levels;

[0152] Optimize algorithm parameters to improve diagnostic accuracy.

[0153] Second experimental mode: digital simulation experiment process

[0154] The experimental purpose is to input the PHM fault simulation signal into the aircraft simulation model to analyze the impact of underlying faults on the whole machine, and to provide a simulation environment for fault prediction and health management algorithm development. The detailed operation steps include:

[0155] Step 1: Prepare the aircraft digital simulation system

[0156] 1.1: Simulation model loading

[0157] Start the aircraft digital simulation system, load the multi-field coupled model, including: six-degree-of-freedom motion model: aircraft rigid body motion based on Lagrange equation; avionics system model: communication network, data processing, human-computer interaction model; electromechanical system model: hydraulic, fuel, environmental control system model; power system model: engine model based on thermodynamic cycle theory.

[0158] 1.2: Simulation environment configuration

[0159] Set the flight scene parameters.

[0160] Step 2: Fault model injection

[0161] 2.1: Power system fault modeling

[0162] Select the target fault type (such as engine surge, fuel pump failure, etc.);

[0163] Establish a fault mathematical model:

[0164]

[0165] Where: Normal thrust output, Multiplicative fault factor, Performance degradation function, Fault noise.

[0166] 2.2: Fault parameter configuration

[0167] Set the fault parameters through the aviation PHM fault injection system, including: fault type: engine surge; fault severity: 0.3 (30% performance degradation); fault occurrence time: 1000 seconds after flight; fault duration: 300 seconds.

[0168] Step 3: Scene simulation test execution

[0169] 3.1: Automatic test script execution

[0170] Run the pre-written XML test script through the flight scene simulation test platform;

[0171] Real-time monitoring of the simulation process, recording key system state parameters.

[0172] 3.2: Multi-system response analysis

[0173] Monitor the impact of faults on each system, including: fuel system: fuel consumption rate changes, fuel supply pressure fluctuations; hydraulic system: hydraulic pump load changes, system pressure abnormalities; flight control system: control command response delay, stability decline; avionics system: sensor data anomalies, communication delay increases.

[0174] Step 4: Cascading impact analysis

[0175] 4.1: Fault propagation path tracking

[0176] Based on the Bayesian network model to analyze fault propagation:

[0177] Fault propagation matrix:

[0178] System state evolution: Identify key fault propagation nodes and paths.

[0179] 4.2: Overall performance evaluation

[0180] Analyze the impact of faults on the overall performance of the aircraft, including: flight envelope reduction, control quality changes, and mission completion capability evaluation.

[0181] Step 5: Data recording and analysis

[0182] 5.1: Simulation data collection

[0183] Record complete simulation process data, including: system state parameter time history, fault propagation timing information, and key performance indicator changes.

[0184] 5.2: Result visualization

[0185] Real-time display through large display systems, including: hydraulic system state diagram, fuel system flowchart, fault impact propagation diagram, and system performance change curve.

[0186] Third experimental mode: virtual-real integrated experimental process

[0187] The experimental goal is to use real component physical experiment platform and virtual simulation platform to jointly inject faults, realize the fusion analysis of real physical signals and virtual environment, and simulate the impact of underlying faults on the whole machine. The detailed operation steps include:

[0188] Step 1: Physical experiment table fault simulation

[0189] 1.1: Bearing progressive degradation fault setting

[0190] On the transmission system multi-fault fusion simulation test bed, the bearing progressive fault is set, including: gradually increasing the radial force (from 100N to 1500N) through the radial loading device; simulate the progressive process of the bearing from normal state to severe wear; set the experimental duration: continuous operation for 8 hours, increase the load every 30 minutes.

[0191] 1.2: Real-time signal acquisition system configuration

[0192] Configure a high-precision sensor array, including:

[0193] Installation location: bearing seat, gear box shell, rotating shaft and other key positions;

[0194] Acquisition parameters: three-axis vibration acceleration, displacement, temperature, and speed;

[0195] Sampling settings: sampling frequency 50kHz, continuous acquisition.

[0196] Step 2: Data interconnection system configuration

[0197] 2.1: Soft bus system settings

[0198] Configure multi-protocol bus interface, including:

[0199] 1553B bus: used for aviation system standard communication, data transmission rate 1Mbps;

[0200] AFDX bus: supports virtual link configuration and end-to-end delay control;

[0201] Ethernet interface: implements TCP / IP protocol stack communication.

[0202] 2.2: Time synchronization configuration

[0203] Establish IEEE 1588 precise clock synchronization.

[0204] Time synchronization algorithm:

[0205] Synchronization accuracy: μs level.

[0206] Step 3: Real-time signal injection virtual platform

[0207] 3.1: Signal preprocessing and format conversion

[0208] Signal conditioning through modular wiring cabinet MWD, including:

[0209] A / D conversion: convert analog signals to digital signals;

[0210] Protocol conversion: convert physical signals to 1553B bus standard format;

[0211] Data packet encapsulation: add timestamp and device identification.

[0212] 3.2: Virtual simulation platform signal injection

[0213] Real-time injection of processed bearing vibration signals into the aircraft digital simulation system, including:

[0214] Signal mapping: map bearing fault signals to the corresponding aircraft engine model;

[0215] Parameter replacement: replace the corresponding parameters in the simulation model with real signals;

[0216] Real-time fusion: maintain time synchronization and amplitude matching of virtual and real signals.

[0217] Step 4: Whole machine digital model deduction

[0218] 4.1: Fault impact propagation analysis

[0219] Based on the injection of real fault signals, deduce the impact in the virtual environment, including:

[0220] Engine system: thrust output fluctuation, vibration transmission path analysis;

[0221] Flight control system: control system response, stability margin change;

[0222] Structural system: vibration load transmission, fatigue damage accumulation.

[0223] 4.2: Multi-level impact analysis

[0224] Analyze the impact chain of the fault from the component level to the system level:

[0225] Bearing fault → engine vibration → airframe structural response → flight control system disturbance → flight quality degradation.

[0226] Step 5: Algorithm platform comprehensive analysis

[0227] 5.1: Residual life prediction algorithm verification

[0228] Transfer the whole machine state data output by virtual deduction to the PHM supercomputing training platform: use LSTM-CNN fusion network for life prediction.

[0229] Prediction model:

[0230] Where: CNN extracts local features, and LSTM captures time sequence dependence.

[0231] 5.2: Algorithm accuracy verification

[0232] Compare the prediction results with the actual fault development trend, including:

[0233] Prediction accuracy evaluation: calculate RMSE, MAE and other evaluation indexes;

[0234] Early warning time evaluation: verify the early warning ability of the algorithm;

[0235] Robustness test: verify the stability of the algorithm under different working conditions.

[0236] Step 6: Closed-loop feedback optimization

[0237] 6.1: Result analysis and feedback

[0238] Analyze the results of virtual-real integration experiments, including:

[0239] Compare the differences between pure virtual simulation and virtual-real fusion results;

[0240] Evaluate the contribution of real signals to the analysis of the impact on the whole machine;

[0241] Identify parts of the virtual model that need to be improved.

[0242] 6.2: System parameter optimization

[0243] Optimize system parameters based on experimental results, including: adjusting the load settings of the physical experiment table, optimizing the parameters of the virtual simulation model, improving the signal processing algorithm, and improving the data fusion accuracy.

[0244] Through the organic combination of these three experimental modes, the synergistic advantages can be achieved, including:

[0245] Full coverage verification: from component-level real fault to whole-machine-level virtual deduction, realizing the full-chain verification of PHM technology;

[0246] Multi-dimensional analysis: combining physical reality, digital flexibility and virtual-real fusion, providing multi-angle fault analysis capability;

[0247] High efficiency research and development: three modes can be independently operated or combined, greatly improving experimental efficiency and research and development speed;

[0248] Standardized process: a repeatable and scalable PHM experimental method system is established, providing important support for the development of aviation equipment health management technology.

[0249] Example 1: Aviation engine fault simulation experiment

[0250] Based on the virtual-real integrated experimental platform of the present application, an aviation engine inner-outer dual rotor fault simulation experiment is carried out:

[0251] 1. Physical experiment table setting: set up a blade crack fault on the aviation engine inner-outer dual rotor simulation experiment table.

[0252] 2. Signal acquisition: Collect real failure signal data through sensor system.

[0253] 3. Virtual environment injection: Inject the collected failure signal into the aircraft digital simulation system through the data interaction system.

[0254] 4. Whole machine impact analysis: Analyze the impact of engine failure on the flight performance of the whole machine in the virtual simulation environment.

[0255] 5. PHM algorithm verification: Based on the obtained failure data and impact analysis results, verify the effectiveness of the failure diagnosis and prediction algorithm.

[0256] Example 2: Transmission system multi-fault fusion experiment

[0257] 1. Multi-fault setting: Set gear box failure and bearing failure on the transmission system physical experiment table.

[0258] 2. Fault injection: Inject the failure signal into the aircraft scene simulation test platform through the aviation PHM fault injection system.

[0259] 3. System level analysis: Analyze the impact of multi-fault coupling on the overall health status of aviation equipment.

[0260] 4. Algorithm optimization: Optimize the multi-fault diagnosis algorithm and health assessment model based on the experimental results.

[0261] Table 1: Comparison table of physical meaning of variables in aircraft six-degree-of-freedom motion equation

[0262] Variable Symbol Physical Meaning (Unit) m Mass (kg) u, v, w Velocity components along x, y, z axes in body-fixed coordinate system (m / s) p, q, r Angular velocity components about x, y, z axes (rad / s) X, Y, Z External forces along x, y, z axes (N) Ixx, Iyy, Izz Moments of inertia about x, y, z axes (kg·m²) Ixy, Ixz, Iyz Products of inertia (kg·m²) L, M, N External moments about x, y, z axes (N·m)

[0263] Table 2: Comparison table of physical meaning of variables in engine thrust model

[0264]

[0265] Table 3: Comparison table of physical meaning of variables in hydraulic system equation

[0266] ρ Liquid density (kg / m³) v Fluid velocity vector (m / s) t Time (s) p Pressure (Pa) μ Viscosity coefficient (Pa·s) g Gravity acceleration (m / s²) F Total actuator output force (N) A Effective area of actuator working chamber (m²) F load ]]> External load on actuator (N)

[0267] Table 4: Comparison table of physical meaning of variables in hydraulic system equation

[0268] S fault (t)]]> System signals under fault conditions (e.g. sensor outputs, system state variables, units depend on specific signals) S normal (t)]]> System signals under normal conditions (same as above) α(t) Multiplicative (amplitude correction) fault factor (dimensionless, usually 0 or a number less than 1) f deg (t)]]> Degradation function describing the degree of degradation of device or component performance over time (dimensionless or depend on specific scenarios) β(t) Additive (offset) fault factor (units consistent with signals) n fault (t)]]> Fault noise component (units consistent with signals, usually Gaussian white noise, etc.)

[0269] Table 5: Comparison table of physical meaning of variables in failure propagation modeling equation

[0270]

[0271] Table 6: Comparison table of physical meaning of variables in rotor / dynamics modeling equation

[0272]

[0273] Table 7. Variable physical meaning table of gear transmission / vibration modeling equation

[0274]

[0275] Table 8. Variable physical meaning table of data synchronization and fusion algorithm

[0276]

[0277] Important parameter notes:

[0278] : Signal that changes with time under the influence of failure (may be system output or collected sensor signal), such as acceleration, pressure, temperature, or power, etc.

[0279] : Corresponding signal under normal no-failure working condition (reference curve or theoretical value);

[0280] : Multiplicative failure factor, describing the proportion of signal amplitude change over time due to failure;

[0281] : Degradation function, representing the physical model of component degradation over time, such as crack length growth, stiffness reduction, etc.

[0282] : Additive failure factor, representing the direct offset of signal due to failure;

[0283] : Additional noise component caused by failure, usually approximated as random noise.

[0284] Typical reference examples:

[0285] 1. In failure injection modeling, , , , , , Commonly describe the evolution of signal with component degradation and failure.

[0286] 2. X(t), P, U(t) constitute the dynamic propagation correlation of failure state at the system level.

[0287] 3. Dynamics class parameters (such as p, q, r, u, v, w, L, M, N...) are basic variables describing the physical state of the whole aircraft or sub-components.

[0288] 4. Data fusion and synchronization variables (e.g. , , , etc.) are used to enable high-precision data exchange and fusion between soft bus systems and different platforms.

[0289] 5. Parameters in dynamics and structural simulation (e.g. , , etc.) are dedicated to complex multi-body coupling and component-level modeling.

[0290] The parts of the present application not described in detail can refer to the prior art or be known to those skilled in the art, and the embodiments are not limited in this regard, and will not be described in detail here.

[0291] The embodiments of the present application are described above in conjunction with the drawings, but the present application is not limited to the specific embodiments described above, and the specific embodiments described above are merely illustrative and not limiting. Those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, and all belong to the protection of the present application.

Claims

1. An aerial equipment PHM virtual-real integration experimental platform, characterized in that, The platform comprises: a component-level PHM physical experiment module configured to implement PHM physical experiments of important components of an aircraft and collect test data; and a whole-machine-level PHM virtual simulation experiment module configured to obtain the test data of the component-level PHM physical experiment module, perform digital simulation of the whole machine and a scene of the aircraft, and inject corresponding PHM faults during the simulation.

2. The aerial equipment PHM virtual-real integrated experimental platform according to claim 1, wherein, The component-level PHM physical experiment module comprises: an aero-engine inner and outer dual-rotor simulation experiment table configured to implement simulation of faults such as rotor blade cracks, motor faults, and rotor imbalance; and a transmission system multi-fault fusion simulation experiment table configured to simulate planetary or parallel gear box faults and bearing faults.

3. The aerial equipment PHM virtual-real integrated experimental platform according to claim 1, wherein, The whole-machine-level PHM virtual simulation experiment module comprises: an aircraft digital simulation system configured to implement simulation of main systems of the aircraft and interactive simulation of onboard PHM; an aircraft scene simulation test platform configured to support automatic testing, experimental script making, test process monitoring, and fault injection functions; and an aviation PHM fault injection system configured to support various fault simulations of aviation systems and implement multi-system fault characteristic simulation.

4. An aerial equipment PHM virtual-real integration experiment method, characterized in that, The method is implemented by using the aviation equipment PHM virtual-real integration experiment platform of any one of claims 1-3. The method comprises a physical platform experiment process, a digital simulation experiment process, and a virtual-real integration experiment process.

5. The aviation equipment PHM virtual-real integration experimental method according to claim 4, characterized in that, The physical platform experiment process comprises: S1, setting a gear box fault on the transmission system physical experiment table and collecting vibration and rotation speed data through a sensor; S2, processing the data collected in S1, extracting fault features, and verifying a diagnosis algorithm.

6. The aviation equipment PHM virtual-real integration experimental method according to claim 4, characterized in that, The digital simulation experiment process comprises: S1, injecting a power system fault model in the aircraft digital simulation system; S2, simulating flight conditions through the scene test platform, generating whole-machine-level fault data, and analyzing cascading effects of the fault on fuel systems and hydraulic systems.

7. The experimental method for the virtual-real integration of the airborne equipment PHM according to claim 4, characterized in that, The virtual-real integration experiment process comprises: S1, simulating a bearing progressive degradation fault on the physical experiment table and collecting real-time vibration signals; S2, injecting the real-time vibration signals into the virtual simulation platform to drive the whole-machine digital model to deduce effects of the fault on the flight control system; S3, performing residual life prediction on the deduced whole-machine state data to verify accuracy of the algorithm.

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