Engine fault diagnosis methods, devices, equipment, and media based on large models

Abstract

This application provides a method, apparatus, device, and medium for engine fault diagnosis based on a large model, relating to the field of engine technology. The method includes: acquiring multi-source data of the engine to be diagnosed, extracting fault description information, and inputting the fault description information into a judgment model; when an acquisition command is output, acquiring engine operating data under idling conditions; calculating the average value of the acquired engine operating data and converting it into a natural language description of the operating data; merging the natural language descriptions of the multi-source data with the natural language descriptions of the operating data to obtain a final natural language description; inputting the final natural language description into a pre-trained model and outputting feature vector data; inputting the feature vector data into a pre-trained engine fault diagnosis model and outputting the fault causative component and repair suggestions. This application can utilize more source data for diagnosis, achieving faster and more accurate fault causative component localization.

Description

Technical Field

[0001] This application relates to the field of engine technology, specifically to an engine fault diagnosis method, device, diagnostic equipment, and storage medium based on a large model. Background Technology

[0002] In industrial production and transportation, engines serve as core power units, and their operational status directly determines the overall efficiency and safety performance of the equipment. Engine failure not only leads to equipment downtime and significant economic losses but can also trigger safety accidents, threatening personnel and property. Therefore, quickly and accurately identifying the root causes of frequent engine failures and then conducting targeted troubleshooting and repair work has always been a core requirement in the field of engine maintenance.

[0003] With the increasing penetration of intelligent technologies into the industrial field, data- and model-based fault diagnosis methods are gradually replacing traditional diagnostic methods that rely on human experience, becoming the mainstream technology for determining the causative components of engine faults. Existing technologies have disclosed a method for determining the causative components of frequently occurring engine faults. This method acquires the current engine's work order, basic information, maintenance report, and sales records. It extracts the current engine fault information from the work order and inputs this fault information, basic information, maintenance report, and sales records into a pre-trained model for determining the causative components of frequently occurring engine faults. The model outputs the causative components of the current frequently occurring engine faults and their corresponding fault probabilities. Finally, based on the fault probabilities, the target causative component is determined, and component fault investigation information is generated. This aims to achieve efficient and accurate diagnosis of the causative components of frequently occurring engine faults, providing guidance for subsequent maintenance work.

[0004] However, in practical applications, the aforementioned existing technologies still have significant shortcomings, which greatly limit their fault diagnosis effectiveness and performance ceiling, making it difficult to meet the high precision and high reliability requirements of engine fault diagnosis under complex operating conditions. Specifically, this is reflected in the following two aspects:

[0005] On the one hand, insufficient data diversity leads to poor fault diagnosis results. Current technologies only use current engine fault information, current engine basic information, current engine maintenance reports, and current engine sales records as data input sources for the model. This type of data contains relatively limited information dimensions and cannot comprehensively and effectively characterize the complex features of engine faults. For example, engine faults are often closely related to long-term operating condition changes, environmental influences, and component aging trends. However, existing input data lacks information reflecting these dynamic changes, making it difficult for the model to capture the potential correlation factors leading to the fault. When facing atypical faults or faults induced by multiple factors, the model is prone to bias in judging the cause of the fault due to the limitations of the input information, resulting in a significant reduction in fault diagnosis effectiveness.

[0006] On the other hand, model selection limits the range of data input, thus restricting the upper limit of model performance. In existing technologies, the selection of models for determining the causes of common engine failures focuses on traditional classification models and conventional machine learning models, such as Support Vector Machine (SVM), Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Backpropagation Neural Networks (BPNN), Probabilistic Neural Networks (PNN), and Random Forest models. These models have significant limitations in data input, supporting only numerical features. For the numerous non-numerical features present in engine operation and maintenance, such as fault descriptions (e.g., "engine leaking water," "abnormal noise"), manufacturer identifiers (e.g., "Factory No. 1," "Factory No. 2"), and equipment usage (e.g., "tractor," "loader"), cumbersome feature encoding preprocessing is required to convert them into numerical forms such as 0, 1, and 2 before inputting them into the model. This process not only increases the complexity of data processing but may also lead to the loss of some feature information. More importantly, these models completely fail to support time-series data as input. Structured time-series data, such as real-time operating data generated during engine operation (e.g., time-series data showing changes in engine speed, oil pressure, and temperature over time) and historical operating condition fluctuation data, contain crucial information about changes in engine operating status and are essential for determining the cause of faults. Because the models cannot utilize this type of data, they cannot analyze the development process and causes of faults from a dynamic perspective. This results in insufficient learning of fault characteristics, severely limiting their performance and making them ill-suited for the complex and ever-changing fault diagnosis scenarios of engines.

[0007] In summary, existing methods for determining the causes of common engine failures suffer from insufficient data diversity and limitations in model selection, making it impossible to fully mine fault-related information and effectively utilize multiple types of data. Consequently, the fault diagnosis effect and performance cannot meet actual needs. There is an urgent need for a technical solution that can overcome the above-mentioned shortcomings in order to improve the accuracy and reliability of determining the causes of common engine failures. Summary of the Invention

[0008] The purpose of this application is to provide an engine fault diagnosis method, device, diagnostic equipment, and storage medium based on a large model, so as to improve the accuracy and reliability of determining the causes of common engine faults.

[0009] In a first aspect, embodiments of this application provide an engine fault diagnosis method based on a large model, comprising:

[0010] Acquire multi-source data of the engine to be diagnosed and integrate the multi-source data into a wide table; the multi-source data includes engine basic data, fault code basic information, work order information, sales records and engine repair reports;

[0011] Fault description information is extracted from the wide table data and input into a pre-trained engine operation data acquisition and judgment model. The model outputs an acquisition command or a non-acquisition command. When an acquisition command is output, the engine to be diagnosed is started and engine operation data under idling conditions is acquired. The engine operation data is time-series structured data.

[0012] The average value of the collected engine operating data is calculated according to a preset time window, and then the average value is converted into a natural language description of the operating data and stored.

[0013] The wide table data is processed into text to obtain a natural language description of the multi-source data. The natural language description of the multi-source data is merged with the natural language description of the running data to obtain the final natural language description. The final natural language description is input into the pre-trained model to output feature vector data.

[0014] The feature vector data is input into a pre-trained engine fault diagnosis model, which outputs the fault-causing component, the corresponding fault probability, and maintenance suggestions.

[0015] In one possible implementation, the engine basic data includes the engine number, engine series, manufacturer, and production date;

[0016] The basic information of the fault code includes the fault code, the cause of the fault, the faulty component, and the solution to the fault.

[0017] The work order information includes the work order number, fault description, and fault date;

[0018] The sales records include the manufacturing date and the intended use of the equipment;

[0019] The engine repair report includes the work order number, engine serial number, and part name.

[0020] In one possible implementation, the engine operating data includes at least one of engine speed, torque, coolant temperature, and oil pressure.

[0021] In one possible implementation, after the natural language description storing the runtime data, the system further includes:

[0022] A notification message will be displayed indicating that the natural language description conversion is complete.

[0023] In one possible implementation, the pre-trained model employs a Transformer model, a Bag-of-Words model, a Word2Vec model, or a Doc2Vec model.

[0024] Secondly, embodiments of this application provide an engine fault diagnosis device based on a large model, comprising:

[0025] The acquisition module is used to acquire multi-source data of the engine to be diagnosed and integrate the multi-source data into a wide table data; the multi-source data includes engine basic data, fault code basic information, work order information, sales records and engine repair reports;

[0026] The judgment module is used to extract fault description information from the wide table data, input the fault description information into the pre-trained engine operation data acquisition and judgment model, and output acquisition command or non-acquisition command; when the acquisition command is output, the engine to be diagnosed is started and engine operation data under idling conditions is acquired, and the engine operation data is time series structured data.

[0027] The conversion module is used to calculate the average value of the collected engine operating data according to a preset time window, then convert the average value into a natural language description of the operating data, and store the natural language description of the operating data.

[0028] The feature module is used to perform textual processing on the wide table data to obtain a natural language description of the multi-source data, merge the natural language description of the multi-source data with the natural language description of the running data to obtain a final natural language description, input the final natural language description into the pre-trained model, and output feature vector data.

[0029] The diagnostic module is used to input the feature vector data into a pre-trained engine fault diagnosis model and output the fault cause, the corresponding fault probability, and maintenance suggestions.

[0030] In one possible implementation, the engine basic data includes the engine number, engine series, manufacturer, and production date;

[0031] The basic information of the fault code includes the fault code, the cause of the fault, the faulty component, and the solution to the fault.

[0032] The work order information includes the work order number, fault description, and fault date;

[0033] The sales records include the manufacturing date and the intended use of the equipment;

[0034] The engine repair report includes the work order number, engine serial number, and part name.

[0035] In one possible implementation, the engine operating data includes at least one of engine speed, torque, coolant temperature, and oil pressure.

[0036] In one possible implementation, the conversion module is further configured to: after storing the natural language description of the running data, issue a prompt message indicating that the natural language description conversion is complete.

[0037] In one possible implementation, the pre-trained model employs a Transformer model, a Bag-of-Words model, a Word2Vec model, or a Doc2Vec model.

[0038] Thirdly, embodiments of this application provide a diagnostic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect of this application.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium having computer-readable instructions stored thereon, which can be executed by a processor to implement the method described in the first aspect of this application.

[0040] The engine fault diagnosis method, apparatus, diagnostic equipment, and storage medium based on a large model provided in this application acquire multi-source data of the engine to be diagnosed and integrate the multi-source data into a wide table data; extract fault description information from the wide table data and input the fault description information into a pre-trained engine operation data acquisition and judgment model, outputting an acquisition command or a non-acquisition command; when an acquisition command is output, control the start of the engine to be diagnosed and acquire engine operation data under idling conditions; calculate the average value of the acquired engine operation data according to a preset time window, then convert the average value into a natural language description of the operation data and store the natural language description of the operation data; perform textual processing on the wide table data to obtain the natural language description of the multi-source data, merge it with the natural language description of the operation data to obtain the final natural language description, input the final natural language description into a pre-trained model, and output feature vector data; input the feature vector data into a pre-trained engine fault diagnosis model, and output the fault cause, the corresponding fault probability, and maintenance suggestions. Compared with existing technologies, the engine fault diagnosis model of this application can utilize more source data for diagnosis, especially fault code data and engine operation data, which provides high-value information for fault diagnosis and enables faster and more accurate location of fault-causing components. Attached Figure Description

[0041] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0042] Figure 1 A flowchart of an engine fault diagnosis method based on a large model provided in this application is shown;

[0043] Figure 2 A schematic diagram of the structure of an engine fault diagnosis device based on a large model provided in this application is shown. Detailed Implementation

[0044] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0045] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0046] Furthermore, the terms "first" and "second," etc., are used to distinguish different objects, not to describe a specific order. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to those processes, methods, products, or apparatuses.

[0047] This application provides an engine fault diagnosis method and apparatus based on a large model, a diagnostic device, and a computer-readable storage medium, which will be described below with reference to the accompanying drawings.

[0048] Please refer to Figure 1 The diagram illustrates a flowchart of an engine fault diagnosis method based on a large model, as provided in this application. Figure 1 As shown, the method includes the following steps:

[0049] S101. Obtain multi-source data of the engine to be diagnosed, and integrate the multi-source data into a wide table data; the multi-source data includes engine basic data, fault code basic information, work order information, sales records and engine repair reports;

[0050] Specifically, the fault diagnosis equipment can be connected to the engine's OBD interface to obtain basic engine data, basic fault code information, work order information, sales records, and maintenance reports. The OBD interface is a vehicle diagnostic interface compliant with relevant standards. Through this interface, bidirectional communication with the engine ECU is established, enabling not only the acquisition of multi-source engine data but also the sending of commands to control the engine to enter preset test conditions.

[0051] In practical applications, the OBD interface can integrate a level conversion circuit and an anti-interference module. The level conversion circuit converts the 5V signal output by the ECU into 3.3V for processing by the data acquisition module, and the anti-interference module uses TVS diodes and common-mode inductors to suppress electromagnetic interference.

[0052] The specific content of each data type is as follows:

[0053] The engine's basic data includes the engine number, engine series, manufacturer, and production date;

[0054] The basic information of the fault code includes the fault code, the cause of the fault, the faulty component, and the solution to the fault.

[0055] The work order information includes the work order number, fault description, and fault date;

[0056] The sales records include the manufacturing date and the intended use of the equipment;

[0057] The engine repair report includes the work order number, engine serial number, and part name.

[0058] In the field of data processing and model application, wide-table data is an important form of data organization. Its core characteristic is that it integrates all information related to the same analytical object (such as a specific engine) that was originally scattered across multiple data tables or data sources into a single data table. In short, wide-table data is not a "new type of data," but rather a "data integration model." Its core function is to break down data silos, enrich information dimensions, and provide more comprehensive and centralized input data for engine fault diagnosis models, thereby improving the accuracy of model diagnosis.

[0059] For ease of understanding, sample data of the above wide table data is provided, as shown in Table 1 below.

[0060] Table 1

[0061]

[0062] S102. Extract fault description information from the wide table data, and input the fault description information into the pre-trained engine operation data acquisition and judgment model, and output an acquisition command or a non-acquisition command; when an acquisition command is output, control the start of the engine to be diagnosed and collect engine operation data under idling conditions, wherein the engine operation data is time series structured data.

[0063] For ease of understanding, sample data of fault description information is provided, as shown in Table 2 below.

[0064] Table 2

[0065]

[0066] The fault description information is input into a pre-trained engine operation data acquisition and judgment model to determine whether to collect engine operation data. If so, an indicator light representing "yes" illuminates on the fault diagnosis equipment terminal; otherwise, an indicator light representing "no" illuminates. If the "yes" indicator light illuminates, the engine is started, and engine operation data under idling conditions is collected. The engine operation data is structured data (time series), and the fields to be collected include, but are not limited to, one or more of the following: engine speed, torque, coolant temperature, and oil pressure.

[0067] This embodiment uses an engine operation data acquisition and determination model to determine whether to collect engine operation data, and collects engine operation data as needed.

[0068] S103. Calculate the average value of the collected engine operating data according to a preset time window, then convert the average value into a natural language description of the operating data, and store the natural language description of the operating data.

[0069] For example, the average value of the data collected in the last five minutes can be calculated, and the calculated average value can be converted into a natural language description of the running data through D2T (Data-to-Text) technology.

[0070] In some embodiments, after storing the natural language description of the runtime data, a prompt message indicating that the natural language description conversion is complete can be issued.

[0071] To facilitate understanding, the following example is provided:

[0072] The data is as follows:

[0073] {

[0074] Engine number: 123J456;

[0075] "Rotation speed": [748, 750, 752, 749, 753, 751, 747];

[0076] Water temperature: [99.0, 99.1, 99.1, 99.0, 99.0, 99.1, 99.1];

[0077] Oil pressure: [223, 225, 226, 220, 215, 224, 218]

[0078] }

[0079] The average value of the structured data in the above data was calculated, and the results are as follows:

[0080] {

[0081] Engine number: 123J456;

[0082] "Rotation speed": 750;

[0083] Water temperature: 99.1°C;

[0084] Oil pressure: 222

[0085] }

[0086] The above results were then input into the D2T model and converted into a natural language description: Engine 123J456 has a current average speed of 750 rpm, an average coolant temperature of 99.1 ℃, and an average oil pressure of 222 kPa.

[0087] After completing the above D2T operation, the natural language structure is stored in the fault diagnosis equipment terminal, and an indicator light is used to indicate that the D2T operation has been completed.

[0088] S104. The wide table data is processed into text to obtain a natural language description of the multi-source data. The natural language description of the multi-source data is merged with the natural language description of the running data to obtain a final natural language description. The final natural language description is input into the pre-trained model to output feature vector data.

[0089] Specifically, the pre-trained model can be a Transformer model, a Bag-of-Words model, a Word2Vec model, or a Doc2Vec model, etc.

[0090] For ease of understanding, the following is a natural language description of the combined D2T processing results of the wide table data and engine operating data for engine number 123J456:

[0091] Engine 123J456, part of the WPXNG series, was manufactured in Factory 1 on November 2, 2019, and shipped on January 1, 2020. It is intended for use in construction machinery. The fault is insufficient engine power and vibration, with a projected failure date of January 1, 2025. The fault code is PK256-00, corresponding to single / multi-cylinder misfire. The cause is an ignition system malfunction, affecting the ignition and valve train systems. Troubleshooting steps include checking the system voltage, verifying the main relay output voltage matches the system voltage, ensuring good contact between the main relay and all coil pins, checking the contact between all coil pins and the ECU, checking the spark plug output for proper spark, checking the spark plug gap, and inspecting the wiring harness for water (if present, clean it). Engine cylinder pressure was also checked. On-site engine operating data was collected: average engine speed 750 rpm, average coolant temperature 99.1 ℃, and average oil pressure 222 kPa.

[0092] The combined natural language descriptions are input into a pre-trained Transformer model for vectorization to obtain feature vector data. Commonly used Transformer models include the BERT Embeddings series, BGE Embeddings series, and OpenAI Embeddings series.

[0093] S105. Input the feature vector data into the pre-trained engine fault diagnosis model and output the fault cause, the corresponding fault probability, and maintenance suggestions.

[0094] Specifically, the acquired data is input into a pre-trained engine fault diagnosis model, which outputs the causative components of engine faults, the corresponding fault probabilities, and maintenance recommendations.

[0095] For ease of understanding, the output of the example in step S104 is provided:

[0096] {

[0097] Engine number: 123J456;

[0098] "Cause of failure": ["Spark plug", "Ignition coil", "Excessive valve clearance"];

[0099] "Probability of failure": [0.80, 0.12, 0.07];

[0100] Repair Recommendations: "Disassemble and inspect all spark plugs, checking electrode shape, carbon buildup, and erosion; use an ignition energy analyzer to compare waveforms for each cylinder to identify the weaker cylinder; check the tightness of the spark plug cap and coil connection; measure and adjust valve clearance."

[0101] }

[0102] Repair technicians can conduct troubleshooting and repair based on repair recommendations, achieving efficient engine fault diagnosis and repair execution.

[0103] In the engine fault diagnosis method based on a large model provided in this application embodiment, the engine fault diagnosis model can utilize more source data for diagnosis, especially fault code data and engine operation data, which provides high-value information for fault diagnosis and enables faster and more accurate location of the fault cause.

[0104] Fault codes typically follow standard coding conventions, combined with OEM-defined content, to provide partially standardized information. This helps quickly locate the system or component range and pinpoint the fault area. Fault codes also include a fault description, cause, and solution, supporting rapid repair decisions. OEM-defined content refers to the fact that, in addition to following industry-standard coding conventions, the fault code coding specifications incorporate proprietary rules or supplementary information defined by the engine brand. These brand-specific definitions are tailored to the structure and performance characteristics of their own engine products, allowing fault codes to more accurately match the fault scenarios of that brand's engines.

[0105] Engine operating data is highly diverse, capable of collecting dozens or even hundreds of signals, which enhances the interpretability of fault code information and helps pinpoint the root cause of faults. Using D2T technology to convert this structured engine operating data into natural language descriptions is a key technological aspect enabling the use of engine operating data.

[0106] Existing technologies only utilize basic engine data, work order information, sales records, and maintenance reports. This insufficient information leads to poor implementation effectiveness and fails to meet current needs. This application abandons type feature encoding in existing technologies and instead uses natural language vectorization technology, which possesses stronger semantic capture capabilities, reduces information loss, and contributes to the accuracy of fault diagnosis.

[0107] For ease of understanding, the following example is provided:

[0108] Taking the domestic Plant 1, Domestic Plant 2, and Vietnam Plant 1 from the engine's basic information as examples, we perform type feature encoding and vectorization on each. Type feature encoding encodes the three plants as the numbers 0, 1, and 2 respectively, while vectorization (using the BGE Embedding model as an example) encodes each plant as a 1024-dimensional vector. The distance between these three 1024-dimensional vectors in the vector space represents their correlation; therefore, the vectors corresponding to domestic Plant 1 and Domestic Plant 2 are closer than the vector corresponding to Vietnam Plant 1. However, the type feature encoding of 0, 1, and 2 completely loses the potential information about the domestic and foreign plants, thus completely negating the impact of the differences between domestic and foreign plants on fault diagnosis.

[0109] Therefore, this application can combine engine operating data, fault code information and other relevant data to achieve accurate fault diagnosis and user-friendly maintenance guidance, thereby improving the maintenance efficiency of maintenance technicians.

[0110] In the above embodiments, a large-model-based engine fault diagnosis method is provided. Correspondingly, this application also provides a large-model-based engine fault diagnosis device, which can be implemented through software, hardware, or a combination of both. For example, the large-model-based engine fault diagnosis device may include integrated or separate functional modules or units to perform the corresponding steps in the above methods. Please refer to... Figure 2 This illustration shows a schematic diagram of a large-model-based engine fault diagnosis device provided by some embodiments of this application. Since the device embodiments are basically similar to the method embodiments, the description is relatively simple; relevant details can be found in the description of the method embodiments. The device embodiments described below are merely illustrative.

[0111] like Figure 2As shown, the engine fault diagnosis device 10 based on a large model provided in this application may include:

[0112] The acquisition module 101 is used to acquire multi-source data of the engine to be diagnosed and integrate the multi-source data into a wide table data; the multi-source data includes engine basic data, fault code basic information, work order information, sales records and engine maintenance reports;

[0113] The judgment module 102 is used to extract fault description information from the wide table data, input the fault description information into a pre-trained engine operation data acquisition and judgment model, and output an acquisition command or a non-acquisition command; when an acquisition command is output, the engine to be diagnosed is started and engine operation data under idling conditions is acquired, wherein the engine operation data is time series structured data.

[0114] The conversion module 103 is used to calculate the average value of the collected engine operating data according to a preset time window, then convert the average value into a natural language description of the operating data, and store the natural language description of the operating data.

[0115] The feature module 104 is used to perform textual processing on the wide table data to obtain a natural language description of the multi-source data, merge the natural language description of the multi-source data with the natural language description of the running data to obtain a final natural language description, input the final natural language description into the pre-trained model, and output feature vector data.

[0116] The diagnostic module 105 is used to input the feature vector data into a pre-trained engine fault diagnosis model and output the fault cause, the corresponding fault probability, and maintenance suggestions.

[0117] In one possible implementation, the engine basic data includes the engine number, engine series, manufacturer, and production date;

[0118] The basic information of the fault code includes the fault code, the cause of the fault, the faulty component, and the solution to the fault.

[0119] The work order information includes the work order number, fault description, and fault date;

[0120] The sales records include the manufacturing date and the intended use of the equipment;

[0121] The engine repair report includes the work order number, engine serial number, and part name.

[0122] In one possible implementation, the engine operating data includes at least one of engine speed, torque, coolant temperature, and oil pressure.

[0123] In one possible implementation, the conversion module 103 is further configured to: after storing the natural language description of the running data, issue a prompt message indicating that the natural language description conversion is complete.

[0124] In one possible implementation, the pre-trained model employs a Transformer model, a Bag-of-Words model, a Word2Vec model, or a Doc2Vec model.

[0125] The engine fault diagnosis device based on a large model provided in this application acquires multi-source data of the engine to be diagnosed and integrates the multi-source data into a wide table data; extracts fault description information from the wide table data and inputs the fault description information into a pre-trained engine operation data acquisition and judgment model, outputting an acquisition command or a non-acquisition command; when an acquisition command is output, the device controls the engine to be diagnosed to start and acquires engine operation data under idling conditions; calculates the average value of the acquired engine operation data according to a preset time window, then converts the average value into a natural language description of the operation data and stores the natural language description of the operation data; performs textual processing on the wide table data to obtain the natural language description of the multi-source data, merges it with the natural language description of the operation data to obtain the final natural language description, inputs the final natural language description into a pre-trained model, and outputs feature vector data; inputs the feature vector data into a pre-trained engine fault diagnosis model, and outputs the fault cause, the corresponding fault probability, and maintenance suggestions. Compared with existing technologies, the engine fault diagnosis model of this application can utilize more source data for diagnosis, especially fault code data and engine operation data, which provides high-value information for fault diagnosis and enables faster and more accurate location of fault-causing components.

[0126] This application also provides a diagnostic device corresponding to the engine fault diagnosis method based on a large model provided in the foregoing embodiments. The diagnostic device may be a mobile phone, laptop computer, tablet computer, desktop computer, etc., to execute the engine fault diagnosis method based on a large model described above.

[0127] The diagnostic equipment provided in this application embodiment and the engine fault diagnosis method based on a large model provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0128] This application also provides a computer-readable storage medium corresponding to the engine fault diagnosis method based on a large model provided in the foregoing embodiments, wherein a computer program (i.e., a program product) is stored thereon. When the computer program is run by a processor, it executes the engine fault diagnosis method based on a large model provided in any of the foregoing embodiments.

[0129] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0130] The computer-readable storage medium provided in the above embodiments of this application and the engine fault diagnosis method based on a large model provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0131] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application.

Claims

1. An engine fault diagnosis method based on a large model, characterized in that, include: Acquire multi-source data of the engine to be diagnosed and integrate the multi-source data into a wide table; the multi-source data includes engine basic data, fault code basic information, work order information, sales records and engine repair reports; Fault description information is extracted from the wide table data and input into a pre-trained engine operation data acquisition and judgment model. The model outputs an acquisition command or a non-acquisition command. When an acquisition command is output, the engine to be diagnosed is started and engine operation data under idling conditions is acquired. The engine operation data is time-series structured data. The engine operation data includes at least one of engine speed, torque, water temperature, and oil pressure. The average value of the collected engine operating data is calculated according to a preset time window, and then the average value is converted into a natural language description of the operating data and stored. The wide table data is processed into text to obtain a natural language description of the multi-source data. The natural language description of the multi-source data is merged with the natural language description of the running data to obtain the final natural language description. The final natural language description is input into the pre-trained model to output feature vector data. The feature vector data is input into a pre-trained engine fault diagnosis model, which outputs the fault-causing component, the corresponding fault probability, and maintenance suggestions.

2. The engine fault diagnosis method based on a large model according to claim 1, characterized in that, The engine's basic data includes the engine number, engine series, manufacturer, and production date; The basic information of the fault code includes the fault code, the cause of the fault, the faulty component, and the solution to the fault. The work order information includes the work order number, fault description, and fault date; The sales records include the manufacturing date and the intended use of the equipment; The engine repair report includes the work order number, engine serial number, and part name.

3. The engine fault diagnosis method based on a large model according to claim 1, characterized in that, Following the natural language description storing the operational data, the system further includes: A notification message will be displayed indicating that the natural language description conversion is complete.

4. The engine fault diagnosis method based on a large model according to claim 1, characterized in that, The pre-trained model uses the Transformer model, Bag-of-Words model, Word2Vec model, or Doc2Vec model.

5. An engine fault diagnosis device based on a large model, characterized in that, include: The acquisition module is used to acquire multi-source data of the engine to be diagnosed and integrate the multi-source data into a wide table data; the multi-source data includes engine basic data, fault code basic information, work order information, sales records and engine maintenance reports; The judgment module is used to extract fault description information from the wide table data, input the fault description information into a pre-trained engine operation data acquisition and judgment model, and output a collection command or a non-collection command; when the collection command is output, it controls the engine to be diagnosed to start and collects engine operation data under idling conditions, wherein the engine operation data is time series structured data; the engine operation data includes at least one of engine speed, torque, water temperature and oil pressure. The conversion module is used to calculate the average value of the collected engine operating data according to a preset time window, then convert the average value into a natural language description of the operating data, and store the natural language description of the operating data. The feature module is used to perform textual processing on the wide table data to obtain a natural language description of the multi-source data, merge the natural language description of the multi-source data with the natural language description of the running data to obtain a final natural language description, input the final natural language description into the pre-trained model, and output feature vector data. The diagnostic module is used to input the feature vector data into a pre-trained engine fault diagnosis model and output the fault cause, the corresponding fault probability, and maintenance suggestions.

6. The engine fault diagnosis device based on a large model according to claim 5, characterized in that, The engine's basic data includes the engine number, engine series, manufacturer, and production date; The basic information of the fault code includes the fault code, the cause of the fault, the faulty component, and the solution to the fault. The work order information includes the work order number, fault description, and fault date; The sales records include the manufacturing date and the intended use of the equipment; The engine repair report includes the work order number, engine serial number, and part name.

7. A diagnostic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the method as described in any one of claims 1 to 4.

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