Intelligent maintenance and diagnosis system for aircraft

Abstract

The invention discloses an aircraft intelligent maintenance and diagnosis system, and relates to the technical field of aircraft maintenance and diagnosis, and the system comprises a data collection module which is used for collecting various data in the actual operation of an aircraft. According to the invention, a digital twin model construction module is utilized to accurately simulate the operation state and fault condition of the aircraft, early discovery and accurate diagnosis of potential faults are realized, the pertinence and effectiveness of maintenance are improved, immersive fault experience and maintenance guidance are provided for maintenance personnel through fusion of VR / AR technologies, and the maintenance efficiency is improved. The maintenance accuracy and efficiency are improved, pre-diagnosis and maintenance scheme verification are carried out on the digital twin model, unnecessary disassembly and detection work is avoided, the maintenance time and labor cost are reduced, potential safety hazards can be eliminated in time through accurate fault diagnosis and an effective maintenance scheme, the flight safety of an aircraft is improved, and the safety of the aircraft is improved. And the maintenance scheme is comprehensively evaluated and optimized, so that the performance and the structural integrity of the maintained aircraft are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft maintenance diagnosis, and in particular to an aircraft intelligent maintenance diagnosis system. Background Art

[0002] An aircraft refers to an aircraft that can fly in the atmosphere. Any aircraft must generate lift greater than its own weight to rise into the air. Based on the principle of generating lift, aircraft can be divided into two categories: lighter-than-air aircraft and heavier-than-air aircraft. The former relies on the static buoyancy of air to rise, while the latter relies on aerodynamic force to overcome its own gravity to rise. The basic principle of aircraft rise is aerodynamics. When air and an object are facing each other head-on, the shape of the airflow around the object depends on the shape and flow speed of the object itself. Aircraft generate lift through specific designs (such as wing shape, rotor rotation, etc.) to overcome its own gravity and achieve flight. With the rapid development of the air transportation industry, the number of aircraft and flight frequency are increasing, which puts higher requirements on the safety, reliability and maintenance efficiency of aircraft.

[0003] Traditional aircraft maintenance and diagnosis methods mainly rely on manual inspection, regular maintenance and experience-based fault judgment, which have many limitations. Manual inspection is prone to omissions, regular maintenance may lead to over-maintenance or under-maintenance, and experience-based fault judgment lacks accuracy and scientificity. Manual inspection cannot accurately predict the maintenance effect and possible risks in advance, and often requires multiple attempts and adjustments in actual operations, which not only increases maintenance time and cost, but also may cause potential damage to the structure and performance of the aircraft. Therefore, it is necessary to propose an aircraft intelligent maintenance and diagnosis system to solve the problems in the existing technology. Summary of the invention

[0004] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an aircraft intelligent maintenance and diagnosis system. It can establish a digital twin model of the aircraft, combine VR and AR technologies, and realize efficient simulation, pre-diagnosis and effectiveness verification of the aircraft's operating status and fault conditions, thereby significantly improving the accuracy and efficiency of maintenance.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an aircraft intelligent maintenance and diagnosis system, the system comprising:

[0006] Data acquisition module, used to collect various data during the actual operation of the aircraft, including but not limited to flight parameters, sensor data and structural data;

[0007] The digital twin model building module builds an accurate digital twin model of the aircraft based on the collected data to simulate the physical characteristics and operating behavior of the aircraft;

[0008] The fault simulation and pre-diagnosis module inputs actual operation data into the digital twin model, simulates fault conditions, and performs pre-diagnosis to analyze the causes of faults and possible impacts;

[0009] The maintenance plan verification module simulates the implementation of the proposed maintenance plan on the digital twin model to evaluate the maintenance effect and possible risks;

[0010] The VR / AR interactive module uses VR and AR technologies to provide maintenance personnel with intuitive fault display and maintenance guidance.

[0011] Furthermore, the specific execution steps of the data acquisition module include:

[0012] Carefully select high-precision, high-reliability sensors suitable for monitoring specific aircraft parts and operating parameters, including pressure sensors for measuring hydraulic system pressure, temperature sensors for detecting engine temperature, and vibration sensors for monitoring the vibration of key components;

[0013] Set parameters for the selected sensors, including sampling frequency, data accuracy, and measurement range, to meet the monitoring requirements of different operating parameters, and configure the trigger conditions for data collection;

[0014] Sensors sense and measure physical quantities in real time during aircraft operation, including flight attitude, speed, altitude, engine parameters, structural stress, and fuel consumption;

[0015] The measured analog signal is converted into a digital signal, and the collected raw data is initially filtered to remove noise and interference signals, improve the data quality, and calibrate and normalize the data.

[0016] Furthermore, the specific execution steps of the data acquisition module also include:

[0017] The pre-processed data is transmitted to the ground receiving station and the airborne data storage and processing unit by wireless data transmission;

[0018] Temporary storage of collected data in a local storage device on board the aircraft;

[0019] At the same time, the data is synchronized to the ground server and cloud storage in real time for long-term backup and archiving;

[0020] During the data collection process, the values ​​of key parameters are monitored in real time. Once an abnormal situation is found, an alarm signal is immediately sent to the ground control center and crew members.

[0021] Furthermore, the specific execution steps of the digital twin model construction module include:

[0022] Obtain rich and pre-processed aircraft operation data from the data acquisition module, including information on structure, performance and systems, and classify, integrate and clean the collected data to remove abnormal and erroneous data;

[0023] According to the type, structure and functional characteristics of the aircraft, select appropriate modeling methods and technologies, including physics-based modeling, system-based modeling and hybrid modeling methods that combine the two, and design the overall architecture of the digital twin model, including determining the model's hierarchical structure, module division and the interaction between modules;

[0024] Physical property modeling, which uses physical equations and mechanical principles to accurately model the physical aspects of the aircraft's mechanical structure, material properties, and aerodynamic characteristics, taking into account the aircraft's stress conditions and heat transfer physical processes under different flight conditions to accurately simulate its operating behavior;

[0025] System characteristic modeling: performance modeling of the aircraft's electronic system and power system, including the working principle, parameter configuration and performance indicators of each system, and establishing the association and interaction model between systems to reflect the comprehensive performance of the entire aircraft;

[0026] Compare the simulation results of the initial model with the actual operating data, and adjust the model parameters to make the model output consistent with the actual data;

[0027] Design a mechanism that can automatically update the model based on real-time data collection to ensure that the model always reflects the latest status of the aircraft;

[0028] Verify the accuracy and effectiveness of the digital twin model by comparing it with the test data and flight records of the actual aircraft;

[0029] The constructed digital twin model is encapsulated and interfaces with other modules are designed to enable smooth data transmission and interaction.

[0030] Furthermore, the digital twin model building module includes the following calculation steps when modeling physical properties:

[0031] For the force analysis of aircraft structure under different flight conditions, the total force F is calculated using the following formula: total : F total =F aerodynamic +F gravity +F inertia +F thrust Among them: F aerodynamic is the aerodynamic force, which is related to the flight speed v, air density ρ, wing area S and aerodynamic coefficient C. The calculation formula is F gravity is gravity, F gravity= mg, where m is the mass of the aircraft and g is the acceleration due to gravity F inertia is the inertial force, which is related to the acceleration a and mass m, F inertia =ma,F thrust is the engine thrust, which is related to the engine power P, speed n and efficiency η.

[0032] For the heat transfer process, consider the two heat transfer modes of convection and radiation, and calculate the temperature change rate of a component Use the following formula: Where: h is the convective heat transfer coefficient, A is the heat transfer area, T env is the ambient temperature, T is the component temperature, σ is the Stefan-Boltzmann constant, is the surface emissivity, m is the component mass, C p is the specific heat capacity.

[0033] Furthermore, the digital twin model building module includes the following calculation steps when modeling system characteristics:

[0034] For the power consumption of aircraft electronic systems P electronics , considering the power requirements and working time of each electronic device, the calculation formula is: P electronics =∑ i P i t i Where P i is the power of the ith electronic device, t i is their working hours;

[0035] For the fuel consumption rate of the power system R fuel , considering the engine power output P engine , fuel calorific value H fuel and efficiency η engine , the calculation formula is:

[0036] Furthermore, the specific execution steps of the fault simulation and pre-diagnosis module include:

[0037] Obtain real-time updated aircraft digital twin model data from the digital twin model building module, including structure, system and operation parameter information;

[0038] According to common operating scenarios and extreme conditions faced by aircraft, multiple simulated operating conditions are set, and specific fault trigger conditions are set for key components and systems, including the degree of wear of components, temperature thresholds and pressure anomalies;

[0039] Under the set working conditions, the occurrence and development process of faults can be simulated by adjusting the parameters in the digital twin model;

[0040] Key characteristic parameters are extracted from the simulation process, including changes in vibration frequency, abnormal temperature rise and pressure fluctuations. Data analysis methods are used to deeply explore and compare these characteristic parameters.

[0041] Furthermore, the specific execution steps of the fault simulation and pre-diagnosis module also include:

[0042] Based on the extracted features and analysis results, a probability-based method is used to identify the fault mode. The current simulation data is compared with the known fault mode library to determine the type, location and severity of the fault. For the fault mode M, a probability-based method is used. j The probability P(M j ) is calculated by the following formula: Where N is the number of characteristic parameters, M is the number of failure modes, and w i is the weight of the i-th feature parameter, S ij is the i-th characteristic parameter in fault mode M j The score under

[0043] Integrate the results of fault mode identification and analysis data to generate a detailed pre-diagnosis report;

[0044] Considering the uncertainty factors in the simulation process, the uncertainty assessment of the pre-diagnosis results is carried out, and the reliability and accuracy of the pre-diagnosis results are expressed in the form of probability distribution;

[0045] The pre-diagnosis report and uncertainty assessment results are output to maintenance personnel, and a visual interface and interactive tools are provided to facilitate users to view the simulation process, analyze data and pre-diagnosis results.

[0046] Furthermore, the specific execution steps of the maintenance plan verification module include:

[0047] Receive the proposed maintenance plan from the maintenance personnel, including the specific operation steps, tools and materials used, and estimated maintenance time;

[0048] Analyze the received maintenance plan and extract key parameters and operation points;

[0049] Based on the operations in the maintenance plan, the aircraft status and parameters are adjusted accordingly in the digital twin model;

[0050] On the adjusted digital twin model, simulated maintenance operations are performed according to the steps and sequence of the maintenance plan;

[0051] Conduct performance evaluation on the digital twin model after simulated maintenance, including but not limited to structural strength, system stability and operational efficiency;

[0052] Analyze potential risks during simulated maintenance and predict possible secondary failures and other adverse consequences;

[0053] Comprehensively evaluate maintenance effects and potential risks, and generate a detailed maintenance plan verification report, which includes feasibility assessment of the maintenance plan, performance prediction after maintenance, risk analysis results, and recommended optimization measures;

[0054] Feedback the verification report to the maintenance personnel, and make necessary optimization and adjustments to the maintenance plan based on the report results.

[0055] Furthermore, the specific execution steps of the VR / AR interaction module include:

[0056] Obtain aircraft data, fault information, and maintenance solutions from the digital twin model, fault simulation and prognostic module, and maintenance solution verification module, and integrate and preprocess the acquired data to make it suitable for display in a VR / AR environment;

[0057] Use virtual reality development tools and techniques to create immersive virtual aircraft environments;

[0058] Superimpose the fault information obtained from the pre-diagnosis on the aircraft components in the virtual environment in an intuitive way;

[0059] In a virtual environment, detailed maintenance instructions are presented to maintenance personnel in the form of dynamic step-by-step demonstrations and text instructions;

[0060] For the augmented reality portion, actual aircraft parts are identified and tracked;

[0061] Provide maintenance personnel with a variety of interactive methods, including gesture operation, voice command, and handle control, so that maintenance personnel can freely view, select, operate, and obtain information in the VR / AR environment;

[0062] Establish real-time data connection with other modules so that the information in the VR / AR environment is updated in time as the actual situation changes;

[0063] Collect operational feedback and opinions from maintenance personnel during the use of the VR / AR interaction module, and record the maintenance personnel's operation process and time data.

[0064] Compared with the existing technology, the aircraft intelligent maintenance and diagnosis system has the following beneficial effects:

[0065] The present invention utilizes digital twin model construction modules to accurately simulate the operating status and fault conditions of the aircraft, realizes early detection and accurate diagnosis of potential faults, improves the pertinence and effectiveness of maintenance, and provides maintenance personnel with an immersive fault experience and intuitive maintenance guidance through the integration of VR / AR technology, thereby improving the accuracy and efficiency of maintenance. Pre-diagnosis and maintenance plan verification are performed on the digital twin model, avoiding unnecessary disassembly and inspection work, reducing maintenance time and labor costs. Accurate fault diagnosis and effective maintenance plans can eliminate potential safety hazards in a timely manner, improve the flight safety of the aircraft, and comprehensively evaluate and optimize the maintenance plan to ensure the performance and structural integrity of the aircraft after maintenance.

[0066] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0068] Figure 1 This is a schematic diagram of the structure of the aircraft intelligent maintenance and diagnosis system. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Embodiment 1

[0071] An aircraft intelligent maintenance and diagnosis system includes: a data acquisition module, a digital twin model construction module, a fault simulation and pre-diagnosis module, a maintenance plan verification module, and a VR / AR interaction module.

[0072] The data acquisition module is used to collect various data during the actual operation of the aircraft, including but not limited to flight parameters, sensor data and structural data. The data acquisition module collects various operating data of the aircraft in real time during the flight through various sensors and data acquisition devices installed on the aircraft, and transmits these data to the digital twin model construction module on the ground through a high-speed communication network.

[0073] The digital twin model construction module builds an accurate aircraft digital twin model based on the collected data to simulate the physical characteristics and operating behavior of the aircraft. After receiving the data, the digital twin model construction module accurately models the physical structure and system performance of the aircraft. At the same time, it continuously updates and optimizes the model according to real-time data to ensure that the model is synchronized with the actual status of the aircraft.

[0074] The fault simulation and pre-diagnosis module inputs actual operating data into the digital twin model, simulates fault conditions, and performs pre-diagnosis, analyzing the causes of faults and possible impacts. It obtains the latest status data from the digital twin model and combines historical fault data to simulate and pre-diagnose possible faults. When a potential fault is detected, a detailed fault report is generated, including the fault type, location, severity, and possible causes, and a warning is issued to maintenance personnel in a timely manner.

[0075] The maintenance plan verification module simulates the implementation of the proposed maintenance plan on the digital twin model to evaluate the maintenance effect and possible risks. The maintenance personnel formulate corresponding maintenance plans based on the fault report and input them into the maintenance plan verification module. This module simulates the implementation of the maintenance plan in detail on the digital twin model to evaluate the maintenance effect and possible risks. If any deficiencies or potential problems are found in the plan, timely feedback will be provided to the maintenance personnel for adjustment and optimization.

[0076] The VR / AR interactive module uses VR and AR technology to provide maintenance personnel with intuitive fault displays and maintenance guidance. Verified maintenance plans are presented to maintenance personnel in an intuitive manner through the VR / AR interactive module. Maintenance personnel can use VR equipment to rehearse maintenance operations in a virtual environment and familiarize themselves with maintenance procedures and precautions. During the actual maintenance process, AR equipment will directly superimpose maintenance guidance information on aircraft components to help maintenance personnel complete maintenance tasks accurately and efficiently.

[0077] By adopting the above-mentioned aircraft intelligent maintenance and diagnosis system, a digital twin model of the aircraft is established, and by combining VR and AR technologies, efficient simulation, pre-diagnosis and effectiveness verification of the aircraft’s operating status and fault conditions can be achieved, thereby significantly improving the accuracy and efficiency of maintenance.

[0078] Embodiment 2

[0079] This embodiment is a further detailed expansion of the first embodiment. This embodiment mainly describes the specific work flow of the aircraft intelligent maintenance and diagnosis system. The specific work flow is as follows.

[0080] First, carefully select high-precision, high-reliability sensors suitable for monitoring specific parts and operating parameters of the aircraft, including pressure sensors for measuring hydraulic system pressure, temperature sensors for detecting engine temperature, and vibration sensors for monitoring the vibration of key components. Ensure that the installation position of the sensors strictly follows the structural design and aerodynamic requirements of the aircraft to ensure the accuracy of data collection without affecting the normal operation and flight safety of the aircraft. Set parameters for the selected sensors, including sampling frequency, data accuracy, and measurement range, to meet the monitoring requirements of different operating parameters, and configure the trigger conditions for data collection, such as starting intensive collection when certain parameters exceed the preset threshold, or performing periodic collection at fixed time intervals.

[0081] Sensors sense and measure physical quantities in real time during aircraft operation, including flight attitude, speed, altitude, engine parameters, structural stress and fuel consumption, convert the measured analog signals into digital signals for subsequent transmission and processing, perform preliminary filtering on the collected raw data, remove noise and interference signals, improve data quality, calibrate and normalize data, and ensure that data collected by different sensors are consistent and comparable.

[0082] Then, wireless data transmission methods such as Bluetooth, Wi-Fi or satellite communication are used to stably and quickly transmit the pre-processed data to the ground receiving station and the onboard data storage and processing unit. The collected data is temporarily stored in the local storage device on the aircraft to prevent data loss. At the same time, the data is synchronized to the ground server and cloud storage in real time for long-term backup and archiving for subsequent analysis and query. During the data collection process, the values ​​of key parameters are monitored in real time. Once an abnormal situation is found, such as data exceeding the normal range or mutation, an alarm signal is immediately sent to the ground control center and crew members so that appropriate measures can be taken in time.

[0083] Then, rich and pre-processed aircraft operation data, including information on structure, performance and system, are obtained, and the collected data is classified, integrated and cleaned to remove abnormal and erroneous data to ensure data quality and availability. According to the type, structure and functional characteristics of the aircraft, appropriate modeling methods and techniques are selected, including physics-based modeling, system-based modeling and hybrid modeling methods that combine the two, and the overall architecture of the digital twin model is designed, including determining the model's hierarchical structure, module division and the interaction between modules.

[0084] Physical property modeling, focusing on the physical aspects of the aircraft's mechanical structure, material properties, and aerodynamic characteristics, uses physical equations and mechanical principles to accurately model the aircraft's force conditions and heat transfer physical processes under different flight conditions to accurately simulate its operating behavior. For the force analysis of the aircraft structure under different flight conditions, the following formula is used to calculate the total force F total : F total =F aerodynamic +F gravity +F inertia +F thrust Among them: F aerodynamic is the aerodynamic force, which is related to the flight speed v, air density ρ, wing area S and aerodynamic coefficient C. The calculation formula is F gravity is gravity, F gravity = mg, where m is the mass of the aircraft and g is the acceleration due to gravity F inertia is the inertial force, which is related to the acceleration a and mass m, F inertia =ma,F thrust is the engine thrust, which is related to the engine power P, speed n and efficiency η. For the heat transfer process, consider the two heat transfer modes of convection and radiation, and calculate the temperature change rate of a component Use the following formula: Where: h is the convective heat transfer coefficient, A is the heat transfer area, T env is the ambient temperature, T is the component temperature, σ is the Stefan-Boltzmann constant, is the surface emissivity, m is the component mass, C p is the specific heat capacity.

[0085] System characteristic modeling, performance modeling of the aircraft's electronic system and power system, including the working principle, parameter configuration and performance indicators of each system, and establishing the association and interaction model between systems to reflect the comprehensive performance of the entire aircraft, and the power consumption P of the aircraft's electronic system electronics , considering the power requirements and working time of each electronic device, the calculation formula is: P electronics =∑ i P i t i Where P i is the power of the ith electronic device, t i is its working time, and the fuel consumption rate R of the power system fuel , considering the engine power output P engine , fuel calorific value H fuel and efficiency η engine , the calculation formula is:

[0086] Next, compare the simulation results of the initial model with the actual operating data. By adjusting the parameters of the model, the output of the model is made to match the actual data. The model parameters are automatically optimized through optimization calculations to improve the accuracy and reliability of the model. A mechanism is designed that can automatically update the model based on real-time collected data to ensure that the model always reflects the latest status of the aircraft. Machine learning algorithms or adaptive control technologies are used to achieve rapid response and adjustment of the model to changes in aircraft operation. The accuracy and effectiveness of the digital twin model are verified by comparing it with the test data and flight records of the actual aircraft. Quantitative indicators such as error rate and similarity are used to evaluate the performance of the model to determine whether it meets the design requirements. The constructed digital twin model is encapsulated for easy calling and interaction in the entire system, and interfaces with other modules (such as fault simulation and pre-diagnosis modules, maintenance plan verification modules, etc.) are designed to enable smooth data transmission and interaction.

[0087] Then, the real-time updated aircraft digital twin model data is obtained from the digital twin model construction module, including structure, system and operating parameter information. At the same time, historical fault data and related maintenance records are accessed to provide more references for fault simulation and analysis. According to the common operating scenarios of the aircraft and the extreme conditions it faces, a variety of simulation conditions are set, such as different combinations of flight altitudes, speeds, attitudes, meteorological conditions, etc. Specific fault trigger conditions are set for key components and systems, including the degree of wear of components, temperature thresholds and pressure abnormalities. Under the set conditions, the occurrence and development process of the fault is simulated by adjusting the parameters in the digital twin model. The simulation algorithm and dynamic model are used to calculate in real time the impact of the fault on the aircraft performance, system operation and structural integrity.

[0088] Then, we extract key characteristic parameters from the simulation process, including vibration frequency changes, abnormal temperature rise and pressure fluctuations. We use data analysis methods to deeply explore and compare these characteristic parameters. For the analysis of vibration frequency changes, the vibration signal is expressed as: Among them, A i is the amplitude of the ith sine wave, f i is the frequency, φ i is the phase, and the change in vibration frequency is calculated by taking the derivative with respect to time: For the evaluation of abnormal temperature rise, the change of temperature over time follows an exponential growth model: T(t) = T0 + (T max -T0)(1-e -kt ) where T0 is the initial temperature, T max is the highest temperature that can be reached in the end, k is the temperature rise rate constant, and the judgment of temperature anomaly is based on the deviation from the normal temperature rise curve: ΔT = T(t) - Tnormal (t) where T normal (t) is the temperature variation curve under normal conditions. For the analysis of pressure fluctuations, the pressure signal is expressed as: Among them, a0 is the DC component, a n and b n are the Fourier coefficients, ω is the angular frequency, and the amplitude of the pressure fluctuation is measured by calculating the root mean square value (RMS):

[0089] Based on the extracted features and analysis results, a probability-based method is used to identify possible failure modes. The current simulation data is compared with the known failure mode library to determine the type, location and severity of the failure. For failure mode M, a probability-based method is used. j The probability P(M j ) is calculated by the following formula: Where N is the number of characteristic parameters, M is the number of failure modes, and w i is the weight of the i-th feature parameter, S ij is the i-th characteristic parameter in fault mode M j The report includes the preliminary judgment of the fault, the possible cause analysis, the potential impact assessment on the operation of the aircraft, and the recommended further detection and diagnostic measures.

[0090] Considering the uncertainty factors in the simulation process, such as model error, parameter estimation deviation, and the range of change of environmental conditions, the uncertainty assessment of the pre-diagnosis results is carried out, and the reliability and accuracy of the pre-diagnosis results are expressed in the form of probability distribution. The pre-diagnosis report and uncertainty assessment results are output to maintenance personnel, and a visual interface and interactive tools are provided to facilitate users to view the simulation process, analyze data and pre-diagnosis results for further discussion and decision-making.

[0091] Next, the proposed maintenance plan is received from the maintenance personnel, including the specific maintenance operation steps, tools and materials used, and the estimated maintenance time. The received maintenance plan is parsed, and key parameters and operation points are extracted, including the maintenance location, maintenance actions, specifications and models of replaced parts. According to the operations in the maintenance plan, the status and parameters of the aircraft are adjusted accordingly in the digital twin model. For example, if the plan involves replacing a certain component, the properties and performance parameters of the component are updated in the model. On the adjusted digital twin model, the maintenance operations are simulated according to the steps and sequence of the maintenance plan. Simulation technology is used to simulate the interaction between tools and components, and the disassembly and installation process of components.

[0092] Then, the performance of the digital twin model after the simulated maintenance is evaluated, including but not limited to structural strength, system stability and operating efficiency. The key performance indicators before and after maintenance are compared, the degree of improvement and whether the expected goals have been achieved are calculated, and the potential risks in the simulated maintenance process are analyzed, such as component incompatibility, the impact of maintenance operations on surrounding structures, etc., and the possible secondary failures and other adverse consequences are predicted. The maintenance effect and potential risks are comprehensively evaluated, and a detailed maintenance plan verification report is generated. The report content includes a feasibility assessment of the maintenance plan, performance prediction after maintenance, risk analysis results, and recommended optimization measures. The verification report is fed back to the maintenance personnel, and according to the report results, the maintenance plan is optimized and adjusted as necessary to ensure its effectiveness and safety in actual applications.

[0093] Finally, the VR / AR interaction module obtains aircraft data, fault information and maintenance plans from the digital twin model, fault simulation and pre-diagnosis module and maintenance plan verification module, integrates and pre-processes the acquired data to make it suitable for display in the VR / AR environment, and uses virtual reality development tools and technologies to create an immersive virtual aircraft environment. According to the size, shape and layout of the actual aircraft, the virtual aircraft model and scene are accurately constructed, and the fault information obtained from the pre-diagnosis, such as fault location, type, severity, etc., is superimposed on the aircraft components in the virtual environment in an intuitive way, and the fault location is highlighted through highlighting, color change and label prompts.

[0094] In the virtual environment, detailed maintenance instructions are presented to maintenance personnel in the form of dynamic step-by-step demonstrations and text instructions. Arrows, animations and other elements are used to guide maintenance personnel in the order and direction of operations. For the augmented reality part, actual aircraft components are identified and tracked to ensure that the AR device can accurately match and overlay virtual maintenance information with actual components. A variety of interactive methods are provided for maintenance personnel, including gesture operations, voice commands, and handle control, so that maintenance personnel can freely view, select, operate and obtain information in the VR / AR environment, establish real-time data connections with other modules, and update the information in the VR / AR environment in a timely manner as the actual situation changes. For example, when the maintenance plan is adjusted or new fault data is generated, it can be immediately reflected in VR / AR. Switching options between training mode and actual operation mode are provided. In training mode, maintenance personnel can repeatedly practice the maintenance process. In actual operation mode, accurate real-time guidance is provided to maintenance personnel, operational feedback and opinions from maintenance personnel in the process of using VR / AR interactive modules are collected, and maintenance personnel's operation process and time data are recorded for subsequent analysis and improvement.

[0095] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the same elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Aircraft intelligent maintenance and diagnosis system, characterized in that: The system includes: Data acquisition module, used to collect various data during the actual operation of the aircraft, including but not limited to flight parameters, sensor data and structural data; The digital twin model building module builds an accurate digital twin model of the aircraft based on the collected data to simulate the physical characteristics and operating behavior of the aircraft; The fault simulation and pre-diagnosis module inputs actual operation data into the digital twin model, simulates fault conditions, and performs pre-diagnosis to analyze the causes of faults and possible impacts; The maintenance plan verification module simulates the implementation of the proposed maintenance plan on the digital twin model to evaluate the maintenance effect and possible risks; The VR / AR interactive module uses VR and AR technologies to provide maintenance personnel with intuitive fault display and maintenance guidance.

2. The aircraft intelligent maintenance and diagnosis system according to claim 1, characterized in that: The specific execution steps of the data acquisition module include: Carefully select high-precision, high-reliability sensors suitable for monitoring specific aircraft parts and operating parameters, including pressure sensors for measuring hydraulic system pressure, temperature sensors for detecting engine temperature, and vibration sensors for monitoring vibration of key components; Set parameters for the selected sensors, including sampling frequency, data accuracy, and measurement range, to meet the monitoring requirements of different operating parameters, and configure the trigger conditions for data collection; Sensors sense and measure physical quantities in real time during aircraft operation, including flight attitude, speed, altitude, engine parameters, structural stress, and fuel consumption; The measured analog signal is converted into a digital signal, and the collected raw data is initially filtered to remove noise and interference signals, improve the data quality, and calibrate and normalize the data.

3. The aircraft intelligent maintenance and diagnosis system according to claim 2, characterized in that: The specific execution steps of the data acquisition module also include: The pre-processed data is transmitted to the ground receiving station and the airborne data storage and processing unit by wireless data transmission; Temporary storage of collected data in a local storage device on board the aircraft; At the same time, the data is synchronized to the ground server and cloud storage in real time for long-term backup and archiving; During the data collection process, the values ​​of key parameters are monitored in real time. Once an abnormal situation is found, an alarm signal is immediately sent to the ground control center and crew members.

4. The aircraft intelligent maintenance and diagnosis system according to claim 1, characterized in that: The specific execution steps of the digital twin model construction module include: Obtain rich and pre-processed aircraft operation data from the data acquisition module, including information on structure, performance and systems, and classify, integrate and clean the collected data to remove abnormal and erroneous data; According to the type, structure and functional characteristics of the aircraft, select appropriate modeling methods and technologies, including physics-based modeling, system-based modeling and hybrid modeling methods that combine the two, and design the overall architecture of the digital twin model, including determining the model's hierarchical structure, module division and the interaction between modules; Physical property modeling, which uses physical equations and mechanical principles to accurately model the physical aspects of the aircraft's mechanical structure, material properties, and aerodynamic characteristics, taking into account the aircraft's stress conditions and heat transfer physical processes under different flight conditions to accurately simulate its operating behavior; System characteristic modeling: performance modeling of the aircraft's electronic system and power system, including the working principle, parameter configuration and performance indicators of each system, and establishing the association and interaction model between systems to reflect the comprehensive performance of the entire aircraft; Compare the simulation results of the initial model with the actual operating data, and adjust the model parameters to make the model output consistent with the actual data; Design a mechanism that can automatically update the model based on real-time data collection to ensure that the model always reflects the latest status of the aircraft; Verify the accuracy and effectiveness of the digital twin model by comparing it with the test data and flight records of the actual aircraft; The constructed digital twin model is encapsulated and interfaces with other modules are designed to enable smooth data transmission and interaction.

5. The aircraft intelligent maintenance and diagnosis system according to claim 4, characterized in that: The digital twin model building module includes the following calculation steps when modeling physical properties: For the force analysis of aircraft structure under different flight conditions, the total force F is calculated using the following formula: total : F total =F aerodynamic +F gravity +F inertia +F thrust Among them: F aerodynamic is the aerodynamic force, which is related to the flight speed v, air density ρ, wing area S and aerodynamic coefficient C. The calculation formula is F gravity is gravity, F gravity = mg, where m is the mass of the aircraft and g is the acceleration due to gravity F inertia is the inertial force, which is related to the acceleration a and mass m, F inertia =ma,F thrust is the engine thrust, which is related to the engine power P, speed n and efficiency η. For the heat transfer process, consider the two heat transfer modes of convection and radiation, and calculate the temperature change rate of a component Use the following formula: Where: h is the convective heat transfer coefficient, A is the heat transfer area, T env is the ambient temperature, T is the component temperature, σ is the Stefan-Boltzmann constant, is the surface emissivity, m is the component mass, C p is the specific heat capacity.

6. The aircraft intelligent maintenance and diagnosis system according to claim 4, characterized in that: The digital twin model building module includes the following calculation steps when modeling system characteristics: For the power consumption of aircraft electronic systems P electronics , considering the power requirements and working time of each electronic device, the calculation formula is: P electronics =∑ i P i t i Where P i is the power of the ith electronic device, t i is their working hours; For the fuel consumption rate of the power system R fuel , considering the engine power output P engine , fuel calorific value H fuel and efficiency η engine , the calculation formula is:

7. The aircraft intelligent maintenance and diagnosis system according to claim 1, characterized in that: The specific execution steps of the fault simulation and pre-diagnosis module include: Obtain real-time updated aircraft digital twin model data from the digital twin model building module, including structure, system and operation parameter information; According to common operating scenarios and extreme conditions faced by aircraft, multiple simulated operating conditions are set, and specific fault trigger conditions are set for key components and systems, including the degree of wear of components, temperature thresholds and pressure anomalies; Under the set working conditions, the occurrence and development process of faults can be simulated by adjusting the parameters in the digital twin model; Key characteristic parameters are extracted from the simulation process, including changes in vibration frequency, abnormal temperature rise and pressure fluctuations. Data analysis methods are used to deeply explore and compare these characteristic parameters.

8. The aircraft intelligent maintenance and diagnosis system according to claim 7, characterized in that: The specific execution steps of the fault simulation and pre-diagnosis module also include: Based on the extracted features and analysis results, a probability-based method is used to identify the fault mode. The current simulation data is compared with the known fault mode library to determine the type, location and severity of the fault. For the fault mode M, a probability-based method is used. j The probability P(M j ) is calculated by the following formula: Where N is the number of characteristic parameters, M is the number of failure modes, and w i is the weight of the i-th feature parameter, S ij is the i-th characteristic parameter in fault mode M j The score under Integrate the results of fault mode identification and analysis data to generate a detailed pre-diagnosis report; Considering the uncertainty factors in the simulation process, the uncertainty assessment of the pre-diagnosis results is carried out, and the reliability and accuracy of the pre-diagnosis results are expressed in the form of probability distribution; The pre-diagnosis report and uncertainty assessment results are output to maintenance personnel, and a visual interface and interactive tools are provided to facilitate users to view the simulation process, analyze data and pre-diagnosis results.

9. The aircraft intelligent maintenance and diagnosis system according to claim 1, characterized in that: The specific execution steps of the maintenance scheme verification module include: Receive the proposed maintenance plan from the maintenance personnel, including the specific operation steps, tools and materials used, and estimated maintenance time; Analyze the received maintenance plan and extract key parameters and operation points; Based on the operations in the maintenance plan, the aircraft status and parameters are adjusted accordingly in the digital twin model; On the adjusted digital twin model, simulated maintenance operations are performed according to the steps and sequence of the maintenance plan; Conduct performance evaluation on the digital twin model after simulated maintenance, including but not limited to structural strength, system stability and operational efficiency; Analyze potential risks during simulated maintenance and predict possible secondary failures and other adverse consequences; Comprehensively evaluate maintenance effects and potential risks, and generate a detailed maintenance plan verification report, which includes feasibility assessment of the maintenance plan, performance prediction after maintenance, risk analysis results, and recommended optimization measures; Feedback the verification report to the maintenance personnel, and make necessary optimization and adjustments to the maintenance plan based on the report results.

10. The aircraft intelligent maintenance and diagnosis system according to claim 1, characterized in that: The specific execution steps of the VR / AR interaction module include: Obtain aircraft data, fault information, and maintenance solutions from the digital twin model, fault simulation and prognostic module, and maintenance solution verification module, and integrate and preprocess the acquired data to make it suitable for display in a VR / AR environment; Use virtual reality development tools and techniques to create immersive virtual aircraft environments; Superimpose the fault information obtained from the pre-diagnosis on the aircraft components in the virtual environment in an intuitive way; In a virtual environment, detailed maintenance instructions are presented to maintenance personnel in the form of dynamic step-by-step demonstrations and text instructions; For the augmented reality portion, actual aircraft parts are identified and tracked; Provide maintenance personnel with a variety of interactive methods, including gesture operation, voice command, and handle control, so that maintenance personnel can freely view, select, operate, and obtain information in the VR / AR environment; Establish real-time data connection with other modules so that the information in the VR / AR environment is updated in time as the actual situation changes; Collect operational feedback and opinions from maintenance personnel during the use of the VR / AR interaction module, and record the maintenance personnel's operation process and time data.

Images