Aero-engine equipment maintenance fault diagnosis method and device based on deep learning

Abstract

The embodiment of the invention provides an aero-engine equipment maintenance fault diagnosis method and device based on deep learning, and relates to the technical field of aero-engine maintenance, and the method comprises the steps: obtaining aero-engine equipment data, including temperature data, vibration data, pressure data, oil consumption data and noise data; performing data preprocessing on the aero-engine equipment data to obtain aero-engine equipment graph structure data; constructing a GPT-GNN model, and training the GPT-GNN model by using the aero-engine equipment graph structure data to obtain a fault diagnosis model; and performing fault diagnosis and fault prediction based on the fault diagnosis model. According to the technical scheme, fault diagnosis of the aero-engine is realized by using the deep learning algorithm, and the accuracy and the diagnosis efficiency of fault diagnosis are improved.

Description

Technical Field

[0001] The present application relates to the technical field of aircraft engine maintenance, and more specifically, to an aircraft engine equipment maintenance fault diagnosis method and device based on deep learning. Background Art

[0002] As the core power unit of aircraft, the reliability and safety of aircraft engines directly affect the safety of flight and the normal operation of flights. Therefore, the maintenance and fault diagnosis of aircraft engines are particularly important. Traditional methods of aircraft engine fault diagnosis mainly rely on manual experience and mechanical testing, usually in the following aspects:

[0003] 1. Manual inspection: Technicians rely on experience to judge potential faults through visual inspection of equipment and measurement with traditional instruments. The disadvantages of this method are low detection efficiency, strong subjectivity, and easy to ignore minor signs of faults.

[0004] 2. Vibration analysis: Monitor the vibration of various components in aircraft engines to identify potential sources of failure. Although vibration analysis can provide certain diagnostic capabilities, it relies on complex signal processing and empirical models and has limitations in dealing with nonlinear and variable faults.

[0005] 3. Temperature monitoring: Monitor temperature changes through sensors installed in various parts of the engine to infer the status of the equipment. However, the single data source of the temperature sensor may also lead to insufficient information for complex fault diagnosis.

[0006] 4. Rule-based system: Diagnosis is performed based on pre-set failure rules and conditions. Although this method can work effectively under certain conditions, it is difficult to adapt to novel or complex failure modes.

[0007] However, the above traditional fault diagnosis methods have limitations. For example, they rely on manual inspection and have low work efficiency; manual judgment is easily affected by experience and emotions and lacks objectivity; existing rule-based diagnostic methods have limited adaptability to novel fault modes; traditional methods perform poorly when processing high-dimensional and nonlinear data; existing technologies have delays in real-time fault detection; fault diagnosis is limited to single parameters such as temperature or vibration; and the model building process of traditional methods is cumbersome and requires professional knowledge.

[0008] Therefore, there is an urgent need for a new fault diagnosis method to diagnose aircraft engines. Summary of the invention

[0009] The embodiments of the present application provide an aircraft engine equipment maintenance fault diagnosis method and device based on deep learning to solve the technical problems existing in the prior art.

[0010] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0011] According to a first aspect of an embodiment of the present application, a method for diagnosing aircraft engine equipment maintenance faults based on deep learning is provided, comprising:

[0012] Obtain aircraft engine equipment data, including temperature data, vibration data, pressure data, fuel consumption data and noise data;

[0013] Performing data preprocessing on the aircraft engine equipment data to obtain aircraft engine equipment graph structure data;

[0014] Constructing a GPT-GNN model, and using the aircraft engine equipment graph structure data to train the GPT-GNN model to obtain a fault diagnosis model;

[0015] Fault diagnosis and fault prediction are performed based on the fault diagnosis model.

[0016] In some embodiments of the present application, based on the above solution, the step of obtaining aircraft engine equipment data includes:

[0017] Place temperature sensors, vibration sensors, pressure sensors, fuel consumption sensors and noise sensors at key locations of aircraft engines;

[0018] The data collected by each sensor is obtained and stored.

[0019] In some embodiments of the present application, based on the above-mentioned solution, the aircraft engine equipment data is preprocessed to obtain aircraft engine equipment graph structure data, including:

[0020] Performing data cleaning and normalization processing on the aviation engine equipment data;

[0021] The processed data is converted into graph structure data to obtain the aero-engine equipment graph structure data.

[0022] In some embodiments of the present application, based on the aforementioned scheme, the construction of the GPT-GNN model includes:

[0023] Use the pre-trained GPT model to perform preliminary fault description and generate context information;

[0024] Designing a graph neural network model for processing graph structure data of the aircraft engine equipment;

[0025] The context information is integrated with the graph neural network model to obtain the GPT-GNN model.

[0026] In some embodiments of the present application, based on the aforementioned solution, the GPT-GNN model is trained using the aircraft engine equipment graph structure data to obtain a fault diagnosis model, including:

[0027] Dividing the aircraft engine equipment graph structure data into a training set, a validation set and a test set according to a ratio;

[0028] Performing model training on the GPT-GNN model based on the training set;

[0029] Verifying the trained GPT-GNN model based on the verification set, and testing the verified GPT-GNN model based on the test set;

[0030] The GPT-GNN model that has passed verification and testing is selected as the fault diagnosis model.

[0031] In some embodiments of the present application, based on the above solution, fault diagnosis is performed based on the fault diagnosis model, including:

[0032] Collect real-time data of aircraft engine equipment;

[0033] Inputting the real-time data into the fault diagnosis model for analysis;

[0034] A fault diagnosis result is generated based on the output of the fault diagnosis model.

[0035] In some embodiments of the present application, based on the above solution, fault prediction is performed based on the fault diagnosis model, including:

[0036] Collect real-time data of aircraft engine equipment;

[0037] Inputting the real-time data into the fault diagnosis model for analysis;

[0038] Set fault thresholds;

[0039] The output of the fault diagnosis model is compared with the fault threshold, and fault prediction information is generated based on the comparison result.

[0040] In some embodiments of the present application, based on the above solution, it also includes:

[0041] The GPT-GNN model is retrained regularly with new aircraft engine equipment data.

[0042] In some embodiments of the present application, based on the above solution, it also includes:

[0043] The fault diagnosis result is stored in a fault knowledge base, and the fault knowledge base is updated in real time.

[0044] According to a second aspect of an embodiment of the present application, there is provided an aircraft engine equipment maintenance fault diagnosis device based on deep learning, comprising:

[0045] An acquisition unit, used to acquire aircraft engine equipment data, including temperature data, vibration data, pressure data, fuel consumption data and noise data;

[0046] A data preprocessing unit, used for preprocessing the aircraft engine equipment data to obtain aircraft engine equipment graph structure data;

[0047] Construction unit, used to build the GPT-GNN model;

[0048] A training unit, used to train the GPT-GNN model using the aircraft engine equipment graph structure data to obtain a fault diagnosis model;

[0049] The fault diagnosis and prediction unit is used to perform fault diagnosis and fault prediction based on the fault diagnosis model.

[0050] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0051] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including a memory and a processor;

[0052] The memory is used to store computer instructions;

[0053] The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.

[0054] The technical solution of the present application utilizes a deep learning algorithm to implement fault diagnosis of aircraft engines, thereby improving the accuracy and efficiency of fault diagnosis.

[0055] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0057] Figure 1 A schematic diagram of a process flow of a method for diagnosing aircraft engine equipment maintenance faults based on deep learning according to an embodiment of the present application is shown;

[0058] Figure 2 A block diagram of a deep learning-based aircraft engine equipment maintenance fault diagnosis device according to an embodiment of the present application is shown;

[0059] Figure 3 A block diagram of an electronic device according to an embodiment of the present application is shown;

[0060] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0062] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0063] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0064] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0065] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment 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.

[0066] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0067] See also Figure 1 , showing a flow chart of a method for diagnosing aircraft engine equipment maintenance faults based on deep learning according to an embodiment of the present application.

[0068] like Figure 1 As shown, a method for diagnosing aircraft engine equipment maintenance faults based on deep learning is presented, which specifically includes steps S100 to S400.

[0069] refer to Figure 1 , step S100, obtaining aircraft engine equipment data, including temperature data, vibration data, pressure data, fuel consumption data and noise data.

[0070] In some feasible embodiments, based on the above solution, the step of obtaining the aircraft engine equipment data includes:

[0071] Place temperature sensors, vibration sensors, pressure sensors, fuel consumption sensors and noise sensors at key locations of aircraft engines;

[0072] The data collected by each sensor is obtained and stored.

[0073] It should be noted that traditional methods usually rely on a single data source, which results in insufficient information acquisition and affects the diagnostic effect. However, this embodiment integrates multiple sensors to acquire multiple data, thereby improving the comprehensive data analysis capability, so that the final diagnostic model can comprehensively monitor the device status.

[0074] Continue to refer Figure 1 , step S200, preprocessing the aircraft engine equipment data to obtain aircraft engine equipment graph structure data.

[0075] In some feasible embodiments, based on the above solution, the aircraft engine equipment data is preprocessed to obtain aircraft engine equipment graph structure data, including:

[0076] Performing data cleaning and normalization processing on the aviation engine equipment data;

[0077] The processed data is converted into graph structure data to obtain the aero-engine equipment graph structure data.

[0078] It can be understood that in the graph structure data, each node represents sensor data, and the edge represents the relationship between different sensors.

[0079] It should be noted that the purpose of converting aircraft engine equipment data into aircraft engine equipment graph structure data is to facilitate the use of data by subsequent deep learning models.

[0080] It should be noted that the data cleaning process specifically includes removing noise and processing outliers.

[0081] Continue to refer Figure 1 , step S300, constructing a GPT-GNN model, and using the aircraft engine equipment graph structure data to train the GPT-GNN model to obtain a fault diagnosis model.

[0082] In some feasible embodiments, based on the above scheme, constructing the GPT-GNN model includes:

[0083] Use the pre-trained GPT model to perform preliminary fault description and generate context information;

[0084] Designing a graph neural network model for processing graph structure data of the aircraft engine equipment;

[0085] The context information is integrated with the graph neural network model to obtain the GPT-GNN model.

[0086] It should be noted that in this application, the GPT model refers to a generative pre-training model, and the GNN model refers to a graph neural network model.

[0087] It is understandable that the traditional manual inspection and mechanical inspection process is tedious and time-consuming. However, this method uses deep learning technology to quickly analyze a large amount of sensor data, shorten fault diagnosis time, and improve overall diagnostic efficiency.

[0088] In addition, traditional methods often rely on manual experience and simple rules, which can easily lead to misjudgment or missed judgment. This method uses deep learning algorithms to automatically identify and classify complex fault modes, greatly improving the accuracy of diagnosis.

[0089] In some feasible embodiments, based on the above scheme, the GPT-GNN model is trained using the aircraft engine equipment graph structure data to obtain a fault diagnosis model, including:

[0090] Dividing the aircraft engine equipment graph structure data into a training set, a validation set and a test set according to a ratio;

[0091] Performing model training on the GPT-GNN model based on the training set;

[0092] Verifying the trained GPT-GNN model based on the verification set, and testing the verified GPT-GNN model based on the test set;

[0093] The GPT-GNN model that has passed verification and testing is selected as the fault diagnosis model.

[0094] Continue to refer Figure 1 , step S400, performing fault diagnosis and fault prediction based on the fault diagnosis model.

[0095] In some feasible embodiments, based on the above solution, fault diagnosis is performed based on the fault diagnosis model, including:

[0096] Collect real-time data of aircraft engine equipment;

[0097] Inputting the real-time data into the fault diagnosis model for analysis;

[0098] A fault diagnosis result is generated based on the output of the fault diagnosis model.

[0099] In some feasible embodiments, based on the above solution, fault prediction is performed based on the fault diagnosis model, including:

[0100] Collect real-time data of aircraft engine equipment;

[0101] Inputting the real-time data into the fault diagnosis model for analysis;

[0102] Set fault thresholds;

[0103] The output of the fault diagnosis model is compared with the fault threshold, and fault prediction information is generated based on the comparison result.

[0104] It should be noted that traditional methods often have delays in fault identification, which affects emergency response. This technical solution can quickly detect potential faults and issue warnings through real-time data flow analysis, reducing the risk of accidents.

[0105] In some feasible embodiments, based on the above solution, it also includes:

[0106] The GPT-GNN model is retrained regularly with new aircraft engine equipment data.

[0107] It should be noted that this step can maintain the diagnostic accuracy and adaptability of the model.

[0108] In some feasible embodiments, based on the above solution, it also includes:

[0109] The fault diagnosis result is stored in a fault knowledge base, and the fault knowledge base is updated in real time.

[0110] It should be noted that this step can improve the learning ability and fault identification ability of the model.

[0111] In summary, the deep learning-based aviation engine equipment maintenance fault diagnosis method proposed in this technical solution helps to improve the safety and reliability of air transportation, and meets the needs of the modern aviation industry for efficient, accurate and real-time maintenance.

[0112] The following describes an embodiment of the device of the present application, which can be used to execute a method for diagnosing aircraft engine equipment maintenance faults based on deep learning in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the above method of the present application.

[0113] Reference Figure 2 As shown, according to an embodiment of the present application, a deep learning-based aircraft engine equipment maintenance fault diagnosis device 200 includes:

[0114] The acquisition unit 201 is used to acquire aircraft engine equipment data, including temperature data, vibration data, pressure data, fuel consumption data and noise data;

[0115] A data preprocessing unit 202 is used to preprocess the aircraft engine equipment data to obtain aircraft engine equipment graph structure data;

[0116] A construction unit 203, used to construct a GPT-GNN model;

[0117] A training unit 204 is used to train the GPT-GNN model using the aircraft engine equipment graph structure data to obtain a fault diagnosis model;

[0118] The fault diagnosis and prediction unit 205 is used to perform fault diagnosis and fault prediction based on the fault diagnosis model.

[0119] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of the above-mentioned deep learning-based aviation engine equipment maintenance fault diagnosis method are implemented.

[0120] Since the electronic device introduced in this embodiment is a device used to implement an aircraft engine equipment maintenance fault diagnosis device based on deep learning in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0121] During the specific implementation process, when the computer program 311 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0122] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0123] It should be noted that Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0125] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0126] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0127] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0128] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0130] As another aspect, the present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for diagnosing aircraft engine equipment maintenance faults based on deep learning described in the above embodiment.

[0131] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements a method for diagnosing aircraft engine equipment maintenance faults based on deep learning described in the above embodiment.

[0132] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0133] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0134] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for aircraft engine equipment maintenance fault diagnosis based on deep learning, characterized in that: include: Obtain aircraft engine equipment data, including temperature data, vibration data, pressure data, fuel consumption data and noise data; Performing data preprocessing on the aircraft engine equipment data to obtain aircraft engine equipment graph structure data; Constructing a GPT-GNN model, and using the aircraft engine equipment graph structure data to train the GPT-GNN model to obtain a fault diagnosis model; Fault diagnosis and fault prediction are performed based on the fault diagnosis model.

2. The method according to claim 1, characterized in that The obtaining of aircraft engine equipment data includes: Place temperature sensors, vibration sensors, pressure sensors, fuel consumption sensors and noise sensors at key locations of aircraft engines; The data collected by each sensor is obtained and stored.

3. The method according to claim 1, characterized in that The step of preprocessing the aircraft engine equipment data to obtain aircraft engine equipment graph structure data includes: Performing data cleaning and normalization processing on the aviation engine equipment data; The processed data is converted into graph structure data to obtain the aero-engine equipment graph structure data.

4. The method according to claim 1, characterized in that: The construction of the GPT-GNN model includes: Use the pre-trained GPT model to perform preliminary fault description and generate context information; Designing a graph neural network model for processing graph structure data of the aircraft engine equipment; The context information is integrated with the graph neural network model to obtain the GPT-GNN model.

5. The method according to claim 1, characterized in that The method of training the GPT-GNN model using the aircraft engine equipment graph structure data to obtain a fault diagnosis model includes: Dividing the aircraft engine equipment graph structure data into a training set, a validation set and a test set according to a ratio; Performing model training on the GPT-GNN model based on the training set; Verifying the trained GPT-GNN model based on the verification set, and testing the verified GPT-GNN model based on the test set; The GPT-GNN model that has passed verification and testing is selected as the fault diagnosis model.

6. The method according to claim 1, characterized in that Performing fault diagnosis based on the fault diagnosis model includes: Collect real-time data of aircraft engine equipment; Inputting the real-time data into the fault diagnosis model for analysis; A fault diagnosis result is generated based on the output of the fault diagnosis model.

7. The method according to claim 1, characterized in that Fault prediction is performed based on the fault diagnosis model, including: Collect real-time data of aircraft engine equipment; Inputting the real-time data into the fault diagnosis model for analysis; Set fault thresholds; The output of the fault diagnosis model is compared with the fault threshold, and fault prediction information is generated based on the comparison result.

8. The method according to claim 1, characterized in that Also includes: The GPT-GNN model is retrained regularly with new aircraft engine equipment data.

9. The method according to claim 1, characterized in that: Also includes: The fault diagnosis result is stored in a fault knowledge base, and the fault knowledge base is updated in real time.

10. A deep learning-based aircraft engine equipment maintenance fault diagnosis device, characterized in that: include: An acquisition unit, used to acquire aircraft engine equipment data, including temperature data, vibration data, pressure data, fuel consumption data and noise data; A data preprocessing unit, used for preprocessing the aircraft engine equipment data to obtain aircraft engine equipment graph structure data; Construction unit, used to build the GPT-GNN model; A training unit, used to train the GPT-GNN model using the aircraft engine equipment graph structure data to obtain a fault diagnosis model; The fault diagnosis and prediction unit is used to perform fault diagnosis and fault prediction based on the fault diagnosis model.

Images