Operation and maintenance detection method and device for gearbox

Abstract

The invention provides an operation and maintenance detection method and device for a gearbox, and relates to the technical field of gearbox testing. According to the method, the digital twin model is constructed, operation and maintenance detection analysis is performed on the sensor data of the gearbox in the current time period, the stress data, the fault detection result and other performance data of the gearbox are determined, and then a plurality of feasible maintenance schemes are generated. And the performance data, the maintenance scheme and the digital twin model of the gearbox are visually displayed to a user, so that the user can visually observe the performance data such as the fault condition of the gearbox and select the corresponding maintenance scheme, the maintenance scheme can be determined without disassembly operation and mass analysis by the user, and the maintenance efficiency is improved. The time for studying and judging the fault of the gear box and determining the maintenance scheme is shortened, the downtime of the gear box is shortened, and the maintenance efficiency of fault processing of the gear box is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gearbox testing, and particularly to an operation and maintenance detection method and device for a gearbox. Background Art

[0002] In the field of intelligent coal mine tunneling equipment, the shape-performance integrated gearbox plays a crucial role. As a key transmission component, its operating state directly affects the overall performance and operation efficiency of the tunneling equipment. To ensure the safe, stable, and reliable operation of the tunneling equipment, it is particularly important to implement real-time and accurate fault detection and timely maintenance for the shape-performance integrated gearbox. However, in practical applications, the fault detection and maintenance processes face various technical challenges.

[0003] Traditional gearbox fault detection and maintenance mainly rely on manual experience and simple vibration and temperature monitoring methods. Although these methods can, to a certain extent, determine whether there is a fault in the gearbox, they often cannot accurately identify the specific fault type, location, and cause. When the traditional monitoring system issues a fault warning or detects an abnormal signal, technicians usually need to stop the machine and disassemble the gearbox for detailed internal inspection. This process is not only time-consuming and laborious but also requires a professional maintenance team and expensive disassembly tools, thus greatly increasing the maintenance cost.

[0004] More seriously, the disassembly process itself may cause unnecessary damage to other intact components of the gearbox, further prolonging the downtime and maintenance cycle. In addition, the analysis process after disassembly highly relies on the experience and intuition of technicians and lacks objective and accurate diagnostic basis, resulting in the accuracy of fault location and the effectiveness of the maintenance plan often being difficult to guarantee. This may not only cause the recurrence of faults, affecting the continuous operation of the tunneling equipment, but also increase additional maintenance costs due to over-maintenance or improper maintenance.

[0005] In summary, the current gearbox fault detection and maintenance process has technical problems such as long maintenance time and low efficiency. Summary of the Invention

[0006] The present invention provides an operation and maintenance detection method and device for a gearbox, which can timely detect and maintain the faults of the gearbox, shorten the downtime during gearbox faults, and improve the maintenance efficiency of gearbox fault handling.

[0007] In a first aspect, the present invention provides an operation and maintenance detection method for a gearbox, the method comprising: acquiring sensor data of the gearbox in the current period; the sensor data includes vibration signals, infrared data, rotational speed, pressure, and load; based on the sensor data, performing feature extraction and transformation to obtain the operating condition data in the current period; based on the operating condition data and a preset digital twin model, performing operation and maintenance detection analysis to determine the performance data of the gearbox in the current period; the performance data includes stress data of each node in the gearbox and a fault detection result; the fault detection result includes a fault type, a faulty component, and a fault location; based on the performance data of the gearbox in the current period, generating multiple feasible maintenance plans; based on the performance data, the multiple feasible maintenance plans, and the digital twin model, generating and displaying a visualization interface of the gearbox.

[0008] In a possible implementation manner, before performing operation and maintenance detection analysis based on the operating condition data and a preset digital twin model to determine the performance data of the gearbox, it further includes: acquiring geometric data of each component of the gearbox, sensor data and stress data during the gearbox test process, and fault characteristics in each time period during the gearbox test process, the fault characteristics including: a fault type, a faulty component, and a fault location; based on the geometric data of each component of the gearbox, constructing a three-dimensional structure module of the gearbox; based on the sensor data during the gearbox test process, determining the operating condition data in multiple time periods; the operating condition data includes operating condition characteristics and temperature change characteristics; based on the operating condition characteristics and stress data in multiple time periods, performing Gaussian regression fitting to obtain a Gaussian process regression module; based on the temperature change characteristics in multiple time periods and the fault characteristics in multiple time periods, performing neural network training to obtain an artificial intelligence module; based on the three-dimensional structure module of the gearbox, the Gaussian process regression module, and the artificial intelligence module, constructing a digital twin model.

[0009] In a possible implementation manner, performing operation and maintenance detection analysis based on the operating condition data and a preset digital twin model to determine the performance data of the gearbox includes: based on the operating condition characteristics in the operating condition data and the Gaussian process regression module in the digital twin model, determining the stress data of each node in the gearbox; based on the temperature change characteristics in the operating condition data and the artificial intelligence model in the digital twin model, determining the fault detection result of the gearbox.

[0010] In a possible implementation, based on the performance data of the gearbox in the current period, multiple feasible maintenance plans are generated, including: performing a performance evaluation on the gearbox based on the performance data in the current period and the performance data of multiple time periods in the pre-stored historical period to determine the performance evaluation level of the gearbox; the performance evaluation levels include normal, minor fault, and serious fault; based on the performance evaluation level of the gearbox and the fault detection result, determining the fault information to be maintained, where the fault information includes the component to be maintained and the fault type; based on the fault information to be maintained and the maintenance strategy library, determining multiple pending maintenance plans, and the pending maintenance plan includes the required maintenance duration, maintenance personnel, maintenance tools, and spare parts; based on the work plan of the gearbox and the required maintenance duration, determining multiple maintenance times; based on the multiple maintenance times and the pending maintenance plan, generating multiple feasible maintenance plans; the feasible maintenance plan includes the maintenance time, maintenance personnel, maintenance tools, and spare parts.

[0011] In a possible implementation, based on the performance data, multiple feasible maintenance plans, and the digital twin model, a visualization interface of the gearbox is generated and displayed, including: updating and annotating the three-dimensional structure module of the gearbox in the digital twin model based on the stress data in the performance data to obtain the updated three-dimensional structure module of the gearbox; rendering the updated three-dimensional structure module of the gearbox based on the fault detection result and the performance evaluation level in the performance data to obtain the rendered three-dimensional structure module; generating a first window based on the rendered three-dimensional structure module; determining the health status of each component of the gearbox based on the fault detection result and the performance evaluation level; generating a second window based on the health status of each component of the gearbox; generating a third window in the form of a two-dimensional chart based on the multiple feasible maintenance plans; generating a visualization interface of the gearbox based on the first window, the second window, and the third window.

[0012] In a possible implementation, rendering the updated three-dimensional structure module of the gearbox based on the fault detection result and the performance evaluation level in the performance data to obtain the rendered three-dimensional structure module includes: if the fault detection result is a minor fault and the performance evaluation level is a minor fault, rendering the faulty component in a first color and rendering the other components except the faulty component in a second color; if the fault detection result is a serious fault or the performance evaluation level is a serious fault, rendering the faulty component in the first color and the second color respectively, and alternately flashing and displaying the faulty component in the first window; rendering the other components except the faulty component in the second color.

[0013] In a possible implementation, based on sensor data, feature extraction and transformation are performed to obtain the operating condition data for the current period, including: performing wavelet transform processing on the vibration signal for the current period to obtain a two-dimensional time-frequency image; the two-dimensional time-frequency image is used to reflect the time-domain information and frequency-domain information of the vibration signal; based on the rotational speed, pressure, and load for the current period, statistical information of each sensor data is calculated; the statistical information includes the average value, variance, median, maximum value, and minimum value; based on each sensor data, the statistical information of each sensor data, and the two-dimensional time-frequency image, feature fusion is performed to obtain the operating condition features; based on the infrared data for the current period, finite element analysis is performed to determine the temperature distribution data inside the gearbox for the current period; based on the temperature distribution data inside the gearbox for the current period, the temperature change characteristics for the current period are determined; based on the operating condition features and the temperature change characteristics, the operating condition data for the current period is determined.

[0014] In a possible implementation, the method further includes: recording the sensor data of the gearbox for multiple periods after maintenance; based on the sensor data for multiple periods, determining the operating condition data for multiple periods; based on the operating condition data for multiple periods and the digital twin model, determining the performance data of the gearbox for multiple periods; based on the performance data of the gearbox for multiple periods, evaluating the health status of the gearbox after maintenance to determine the maintenance effect.

[0015] In a possible implementation, the performance data further includes the failure probability corresponding to the failure type; correspondingly, after determining the performance data of the gearbox for the current period based on the operating condition data and the preset digital twin model through operation and maintenance detection analysis, it further includes: based on the performance data for the current period and the performance data for multiple time periods within the pre-stored historical period, evaluating the health status of the gearbox to determine the health parameters of the gearbox; based on the health parameters of the gearbox, determining the maintenance cycle of each component of the gearbox; based on the maintenance cycle of each component of the gearbox and the work plan of the gearbox, determining the maintenance plan of the gearbox.

[0016] Second aspect, an operation and maintenance detection device for a gearbox is provided in an embodiment of the present invention. The device includes a communication module and a processing module. The communication module is configured to obtain sensor data of the gearbox in the current period. The sensor data includes vibration signals, infrared data, rotational speed, pressure, and load. The processing module is configured to perform feature extraction and conversion based on the sensor data to obtain the operating condition data in the current period. Based on the operating condition data and a preset digital twin model, perform operation and maintenance detection analysis to determine the performance data of the gearbox in the current period. The performance data includes stress data of each node in the gearbox and a fault detection result. The fault detection result includes a fault type, a faulty component, and a fault location. Generate multiple feasible maintenance plans based on the performance data of the gearbox in the current period. Generate and display a visualization interface of the gearbox based on the performance data, multiple feasible maintenance plans, and the digital twin model.

[0017] Third aspect, an electronic device is provided in an embodiment of the present invention. The electronic device includes a memory and a processor. The memory stores a computer program. The processor is configured to execute the steps of the method as described in the first aspect and any possible implementation manner in the first aspect when calling and running the computer program stored in the memory.

[0018] Fourth aspect, a computer-readable storage medium is provided in an embodiment of the present invention. The computer-readable storage medium stores a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method as described in the first aspect and any possible implementation manner in the first aspect.

[0019] The present invention provides an operation and maintenance detection method and device for a gearbox. By constructing a digital twin model, the present invention performs operation and maintenance detection analysis on the sensor data of the gearbox in the current period, determines performance data such as stress data and fault detection results of the gearbox, and then generates multiple feasible maintenance plans. The performance data, maintenance plans, and digital twin model of the gearbox are visually displayed to the user, facilitating the user to intuitively observe performance data such as the fault condition of the gearbox and select corresponding maintenance plans. Without the need for the user to perform disassembly operations and a large amount of analysis, the maintenance plan can be determined, shortening the time for fault judgment and determination of the repair plan for the gearbox, as well as the downtime of the gearbox, and improving the maintenance efficiency of gearbox fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flowchart of an operation and maintenance detection method for a gearbox provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of an operation and maintenance detection device for a gearbox provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0022] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0023] In the description of the present invention, unless otherwise specified, " / " means "or". For example, A / B can represent A or B. The "and / or" herein is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, "at least one" and "a plurality of" mean two or more. The words such as "first" and "second" do not limit the quantity and execution order, and the words such as "first" and "second" do not necessarily limit to be different.

[0024] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific way for easy understanding.

[0025] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the listed steps or modules, but may alternatively include other unlisted steps or modules, or may alternatively include other steps or modules inherent to these processes, methods, products, or devices.

[0026] To make the purpose, technical solution, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the drawings of the present invention.

[0027] Such as Figure 1As shown in the figure, an embodiment of the present invention provides an operation and maintenance detection method for a gearbox. This method includes steps S101 - S105.

[0028] S101. Obtain the sensor data of the gearbox for the current period.

[0029] In an embodiment of the present application, the sensor data includes vibration signals, infrared data, rotational speed, pressure, and load.

[0030] Exemplarily, in an embodiment of the present invention, sensors can be installed at key positions of the gearbox, including vibration sensors, infrared sensors, rotational speed sensors, pressure sensors, and load sensors. The data of these sensors are collected in real time through a data acquisition system to ensure the accuracy and real - time nature of the data. The collected data is pre - processed, including denoising, filtering, outlier processing, etc., to improve the data quality.

[0031] S102. Based on the sensor data, perform feature extraction and transformation to obtain the working condition data for the current period.

[0032] Exemplarily, in an embodiment of the present invention, signal processing techniques and machine learning algorithms can be used to extract features from the sensor data that can reflect the working conditions of the gearbox, such as vibration frequency, amplitude, temperature distribution, pressure change, load condition, etc. The extracted features are converted into a standardized working condition data format for subsequent analysis and processing.

[0033] As a possible implementation manner, step S102 can be specifically implemented as steps S1021–S1022.

[0034] S1021. Perform wavelet transform processing on the vibration signal for the current period to obtain a two - dimensional time - frequency image.

[0035] In some embodiments, the two - dimensional time - frequency image is used to reflect the time - domain information and frequency - domain information of the vibration signal.

[0036] Exemplarily, in an embodiment of the present invention, the vibration signal for the current period can be collected from the sensor. The collected vibration signal is subjected to wavelet transform processing to convert it from the time domain to the time - frequency domain. Appropriate wavelet basis functions and decomposition levels are selected to obtain a clear two - dimensional time - frequency image. According to the results of the wavelet transform, a two - dimensional time - frequency image is generated, which can reflect the time - domain information and frequency - domain information of the vibration signal.

[0037] S1022. Based on the rotational speed, pressure, and load for the current period, calculate the statistical information of each sensor data.

[0038] In some embodiments, the statistical information includes mean, variance, median, maximum value, and minimum value.

[0039] Exemplarily, embodiments of the present invention can collect rotational speed, pressure, and load data for the current period from corresponding sensors. Statistical calculations are performed on the collected data, including calculating statistical information such as the average value, variance, median, maximum value, and minimum value.

[0040] S1023. Based on the data of each sensor, the statistical information of each sensor's data, and the two-dimensional time-frequency image, perform feature fusion to obtain the working condition features.

[0041] Exemplarily, embodiments of the present invention can integrate the collected data of each sensor, the calculated statistical information, and the generated two-dimensional time-frequency image. Extract features related to the working condition of the gearbox from the integrated data. These features may include specific frequency components of the vibration signal, the stability of the rotational speed, the change range of pressure and load, etc. The extracted features are fused to obtain the working condition features that can comprehensively reflect the current working condition of the gearbox.

[0042] S1024. Based on the infrared data for the current period, perform finite element analysis to determine the temperature distribution data inside the gearbox for the current period.

[0043] Exemplarily, embodiments of the present invention can collect infrared data for the current period from an infrared sensor. Perform finite element analysis on the collected infrared data to simulate the temperature distribution inside the gearbox. Consider factors such as the geometric shape of the gearbox, material properties, and heat conduction during the analysis process. Generate the temperature distribution data inside the gearbox for the current period according to the results of the finite element analysis.

[0044] S1025. Based on the temperature distribution data inside the gearbox for the current period, determine the temperature change characteristics for the current period.

[0045] S1026. Based on the working condition features and the temperature change characteristics, determine the working condition data for the current period.

[0046] Exemplarily, embodiments of the present invention can extract features related to the temperature change of the gearbox from the temperature distribution data, such as the highest temperature, the lowest temperature, the temperature gradient, etc. Integrate the extracted working condition features and the temperature change characteristics to obtain the working condition data for the current period. The working condition data should be able to comprehensively reflect the working state and performance of the gearbox during the current period.

[0047] S103. Based on the working condition data and a preset digital twin model, perform operation and maintenance detection analysis to determine the performance data of the gearbox for the current period.

[0048] In the embodiments of the present application, the performance data includes stress data of each node inside the gearbox and the fault detection result; the fault detection result includes the fault type, the faulty component, and the fault location.

[0049] Exemplarily, embodiments of the present invention can pre-construct a digital twin model of the gearbox, which can simulate the actual operation of the gearbox, including the interaction of various components, stress distribution, etc. Input the working condition data into the digital twin model, and through simulation calculation, obtain the performance data of the gearbox at the current time period, including the stress data of each node, fault detection results, etc. Utilize the simulation results of the digital twin model and combine with the fault detection algorithm to identify the fault type, faulty component, and fault location.

[0050] As a possible implementation manner, step S103 can be specifically implemented as steps S1031–S1032.

[0051] S1031. Determine the stress data of each node in the gearbox based on the working condition characteristics in the working condition data and the Gaussian process regression module in the digital twin model.

[0052] Exemplarily, embodiments of the present invention can extract features related to the stress state of the gearbox from the working condition data, such as load magnitude, rotational speed, running time, etc. These features should be able to comprehensively reflect the changes in the working conditions of the gearbox during operation. The Gaussian process regression module integrated in the digital twin model is used to predict the stress data of each node in the gearbox according to the extracted working condition features. Gaussian process regression is a non-parametric Bayesian regression method, suitable for modeling small sample data and non-linear relationships. When applying Gaussian process regression, kernel functions (such as RBF kernel, Matern kernel, etc.) and hyperparameters (such as length scale, noise variance, etc.) need to be set, and these parameters can be optimized through training data. Input the extracted working condition features into the Gaussian process regression module to obtain the stress prediction values of each node in the gearbox. Output the stress data, including information such as the stress magnitude and direction of each node.

[0053] S1032. Determine the fault detection result of the gearbox based on the temperature change characteristics in the working condition data and the artificial intelligence model in the digital twin model.

[0054] Exemplarily, embodiments of the present invention can extract features related to the temperature change of the gearbox from the operating condition data, such as the temperature change trend, the temperature fluctuation range, etc. These features should be able to reflect the thermal state change of the gearbox during operation. The artificial intelligence models (such as deep learning models, support vector machines, etc.) integrated in the digital twin model are used for fault detection based on the extracted temperature change features. When applying the artificial intelligence model, it is necessary to train the model first so that it can identify the differences between the normal state and the fault state. The training data should include the temperature change features in the normal state and the temperature change features in the known fault state. The extracted temperature change features are input into the artificial intelligence model to obtain the fault detection results of the gearbox. The output results should include information such as the fault type, the fault location, and the fault severity. According to the fault detection results, a maintenance plan can be further formulated or other measures can be taken to eliminate the fault.

[0055] S104. Generate multiple feasible maintenance plans based on the performance data of the gearbox in the current period.

[0056] Exemplarily, embodiments of the present invention can formulate multiple feasible maintenance plans according to the performance data and fault detection results of the gearbox, including preventive maintenance, corrective maintenance, improvement maintenance, etc.

[0057] As a possible implementation manner, step S104 can be specifically implemented as steps S1041–S1045.

[0058] S1041. Based on the performance data in the current period and the performance data of multiple time periods in the pre-stored historical period, perform a performance evaluation on the gearbox to determine the performance evaluation level of the gearbox.

[0059] In some embodiments, the performance evaluation levels include normal, minor fault, and severe fault.

[0060] Exemplarily, embodiments of the present invention can compare and analyze the performance data in the current period (such as vibration, temperature, pressure, etc.) with the performance data in the historical period to identify the change trend and abnormal points of the performance data. Use the pre-constructed performance evaluation model (such as a machine learning-based classification model) to perform a performance evaluation on the gearbox. This model can output the performance evaluation level of the gearbox according to the input performance data. According to the output result of the model, the performance evaluation level of the gearbox is divided into normal, minor fault, and severe fault. This level division helps in the formulation of subsequent maintenance plans.

[0061] S1042. Based on the performance evaluation level of the gearbox and the fault detection results, determine the fault information to be maintained.

[0062] In some embodiments, the fault information includes the components to be maintained and the fault type.

[0063] Exemplarily, embodiments of the present invention can utilize a fault detection algorithm (such as a fault diagnosis algorithm based on vibration signals) to detect faults in the gearbox and identify specific fault types and locations. Combine the fault detection results with the performance evaluation level to determine the fault information to be maintained. This includes the components to be maintained (such as bearings, gears, etc.) and the fault types (such as wear, fracture, etc.).

[0064] S1043. Based on the fault information to be maintained and the maintenance strategy library, determine multiple pending maintenance plans.

[0065] In some embodiments, the pending maintenance plans include the required maintenance duration, maintenance personnel, maintenance tools, and spare parts.

[0066] Exemplarily, embodiments of the present invention can pre-construct a maintenance strategy library containing various fault types and their corresponding maintenance strategies. These strategies can include information such as maintenance methods, maintenance tools, and spare parts. According to the fault information to be maintained, select appropriate maintenance strategies from the maintenance strategy library and formulate multiple pending maintenance plans in combination with the actual situation (such as the skill level of maintenance personnel, the availability of maintenance tools, etc.). Each plan should include key information such as the required maintenance duration, maintenance personnel, maintenance tools, and spare parts.

[0067] S1044. Based on the work plan of the gearbox and the required maintenance duration, determine multiple maintenance times.

[0068] Exemplarily, embodiments of the present invention can analyze the work plan of the gearbox, including operating time, downtime, load conditions, etc. This helps to determine a suitable maintenance time window. According to the required maintenance duration and the work plan, determine multiple possible maintenance times. These times should ensure that the gearbox can be shut down for maintenance operations during the maintenance period while minimizing the impact on production.

[0069] S1045. Based on multiple maintenance times and the pending maintenance plans, generate multiple feasible maintenance plans.

[0070] In some embodiments, the feasible maintenance plans include maintenance time, maintenance personnel, maintenance tools, and spare parts.

[0071] Exemplarily, embodiments of the present invention can combine multiple maintenance times with the pending maintenance plans to generate multiple feasible maintenance plans. These plans should include information such as specific maintenance times, maintenance personnel, maintenance tools, and spare parts. Evaluate the generated multiple feasible maintenance plans, considering factors such as maintenance cost, maintenance effect, and impact on production. Finally, select the optimal maintenance plan for implementation.

[0072] S105. Generate and display a visualization interface of the gearbox based on the performance data, multiple feasible maintenance plans, and the digital twin model.

[0073] Exemplarily, design a visualization interface of the gearbox, including a 3D model of the gearbox, display of operating conditions data, performance data charts, fault detection result prompts, display of maintenance plans, etc. Data visualization: Display information such as the performance data, fault detection results, and maintenance plans of the gearbox in the form of graphs, charts, animations, etc. on the visualization interface, facilitating intuitive understanding and analysis by users. Implementation of interactive functions: Implement interactive functions in the visualization interface, such as data query, plan selection, parameter adjustment, etc., so that users can operate and make decisions according to actual needs.

[0074] As a possible implementation, step S105 can be specifically implemented as steps S1051–S1055.

[0075] S1051. Update and label the 3D structure module of the gearbox in the digital twin model based on the stress data in the performance data, to obtain an updated 3D structure module of the gearbox.

[0076] Exemplarily, embodiments of the present invention can map the stress data in the performance data to the 3D structure module of the gearbox in the digital twin model. This generally involves associating the stress values with the corresponding nodes or components in the model. Update the 3D structure module of the gearbox in the digital twin model according to the mapped stress data. The update may include changing colors, transparency, or adding labels to visually display the stress distribution. Add labels to the updated 3D structure module to indicate the magnitude and direction of the stress. The labels can be numbers, arrows, or other visual elements, used to help users understand the stress state.

[0077] S1052. Render the updated 3D structure module of the gearbox based on the fault detection results and performance evaluation levels in the performance data, to obtain a rendered 3D structure module.

[0078] Embodiments of the present invention can map the fault detection results and performance evaluation levels to the updated 3D structure module. This may involve associating different types of faults and evaluation levels with the corresponding components in the model. According to the mapping results, set rendering parameters, such as colors, textures, lighting, etc., to highlight the faulty components and evaluation levels. The selection of rendering parameters should be able to clearly convey the fault information and evaluation levels. Perform the rendering operation to obtain a rendered 3D structure module. The rendered module should be able to visually display the fault state and performance evaluation level of the gearbox.

[0079] Exemplarily, if the fault detection result is a minor fault and the performance evaluation level is a minor fault, the faulty component is rendered in a first color and the other components other than the faulty component are rendered in a second color.

[0080] For example, determine the first color and the second color. The first color is usually selected as a softer color (such as light yellow or green) to indicate a minor fault or a minor performance degradation; the second color is selected as a more neutral or basic color (such as gray or light blue) to represent the other components in the normal state. In the three-dimensional structure module, find the component corresponding to the minor fault detection result and render it in the first color. Render all other components other than the faulty component in the second color.

[0081] Another exemplarily, if the fault detection result is a severe fault or the performance evaluation level is a severe fault, the faulty component is rendered in the first color and the second color respectively, and the faulty component is alternately flashed and displayed in the first window; the other components other than the faulty component are rendered in the second color.

[0082] For example, determine the first color and the second color. In this case, the first color may be selected as a more eye-catching color (such as red or orange) to emphasize the severe fault; the second color is still used to represent the other components in the normal state. Set the flashing effect, including parameters such as the flashing frequency, duration, and brightness change. In the three-dimensional structure module, find the component corresponding to the severe fault detection result or the severe performance evaluation level and render it in the first color. In the first window, make these faulty components alternately flash and display according to the preset flashing parameters to attract the user's attention. Render all other components other than the faulty component in the second color.

[0083] For example, when a certain gear in the gearbox fails, the embodiment of the present invention can render the gear in red and gray respectively. And other components in the gearbox (bearings, other gears, etc.) are all rendered in gray. Then display the gearbox in the first window. The other components in the gearbox that have not failed are all displayed in gray, and the faulty gear is alternately flashed and displayed in red and gray. Thus, the fault warning of the gearbox is realized.

[0084] S1053. Generate a first window based on the rendered three-dimensional structure module.

[0085] Exemplarily, the embodiment of the present invention can create a new window in the visualization interface to display the rendered three-dimensional structure module. The size, position, and layout of the window should be adjusted according to the user's needs and the interface design. Embed the rendered three-dimensional structure module into the first window. Ensure the display quality of the module in the window, including clarity, color, and dynamic effects.

[0086] S1054. Determine the health status of each component of the gearbox based on the fault detection results and the performance evaluation level.

[0087] Exemplarily, embodiments of the present invention can comprehensively evaluate the health status of each component of the gearbox according to the fault detection results and the performance evaluation level. The evaluation may involve comprehensive consideration of the fault type, severity, and evaluation level. Record the evaluation results as the health status data of each component of the gearbox. The data should include information such as component name, health status level, evaluation time, etc.

[0088] S1055. Generate a second window based on the health status of each component of the gearbox.

[0089] Exemplarily, embodiments of the present invention can visualize the health status data of each component of the gearbox so that users can intuitively understand the health status of each component. Visualization may involve using color coding, charts, or text to represent the health status level. Create a new window in the visualization interface to display the health status of each component of the gearbox. The layout and display method of the window should be adjusted according to user needs and interface design.

[0090] S1056. Generate a third window in the form of a two-dimensional chart based on multiple feasible maintenance plans.

[0091] Exemplarily, embodiments of the present invention can visualize multiple feasible maintenance plans in the form of a two-dimensional chart so that users can compare and select the optimal plan. The chart may include bar charts, line charts, pie charts, etc., for representing various aspects of the maintenance plan (such as cost, time, effect, etc.). Create a new window in the visualization interface to display the two-dimensional chart of multiple feasible maintenance plans. Ensure the clarity and readability of the chart in the window.

[0092] S1057. Generate a visualization interface of the gearbox based on the first window, the second window, and the third window.

[0093] Exemplarily, embodiments of the present invention can integrate the first window, the second window, and the third window into a visualization interface. The integration process should ensure reasonable layout and coordination among the windows, and facilitate users to switch and view. Add interactive functions to the visualization interface, such as zooming, rotating, click selection, etc., so that users can more flexibly view and analyze the status and maintenance plan of the gearbox. The interaction design should be simple and intuitive, easy for users to understand and operate. After completing the integration and interaction design, display the final visualization interface of the gearbox. The interface should be able to clearly convey the status, fault information, health status, and maintenance plan of the gearbox, providing comprehensive decision-making support for users.

[0094] The present invention provides an operation and maintenance detection method for a gearbox. By constructing a digital twin model, analyzing the sensor data of the gearbox in the current period, determining performance data such as stress data and fault detection results of the gearbox, and then generating multiple feasible maintenance plans. And the performance data, maintenance plans and digital twin model of the gearbox are visually displayed to the user, facilitating the user to intuitively observe performance data such as the fault condition of the gearbox, and select the corresponding maintenance plan. Without the user having to perform disassembly operations and a large amount of analysis to determine the maintenance plan, the time for fault judgment and determination of the repair plan for the gearbox, as well as the downtime of the gearbox, is shortened, and the maintenance efficiency of gearbox fault handling is improved.

[0095] Optionally, for the operation and maintenance detection method of the gearbox provided by the embodiments of the present invention, before step S103, steps S201-S206 are further included.

[0096] S201. Obtain the geometric data of each component of the gearbox, the sensor data and stress data during the gearbox test, and the fault characteristics in each time period during the gearbox test.

[0097] In some embodiments, the fault characteristics include: fault type, faulty component and fault location.

[0098] Exemplarily, the geometric data usually comes from a CAD (Computer-Aided Design) model or 3D scanning technology. The CAD model provides the precise dimensions and shape information of each component of the gearbox, while 3D scanning technology can obtain the geometric form of the physical gearbox. Data format: Ensure that the obtained geometric data format is compatible with the subsequent modeling software. Common formats include STL, OBJ, IGES, etc. Data verification: Verify the geometric data to ensure the integrity and accuracy of the data, and avoid errors in subsequent modeling.

[0099] Exemplarily, design a gearbox test plan, clarify the test purpose, test conditions, sensor arrangement, etc. During the test, use sensors to collect parameters such as vibration, temperature, pressure, load, etc. of the gearbox in real time, and record the stress data. Ensure that the data of all sensors are synchronized in time for subsequent analysis of the performance of the gearbox under different working conditions.

[0100] Exemplarily, during the test, record the time point, fault type, faulty component and fault location when the gearbox fails. Fault classification: Classify the faults and clarify the characteristics and possible causes of each fault. Fault data collation: Collate the fault data into a structured format for subsequent analysis and modeling.

[0101] S202. Based on the geometric data of each component of the gearbox, construct a three-dimensional structure module of the gearbox.

[0102] Exemplarily, select appropriate 3D modeling software, such as SolidWorks, Autodesk Inventor, etc. According to the geometric data, construct the 3D structure module of the gearbox in the modeling software, including the assembly relationship and relative position of each component. Verify the constructed 3D structure module to ensure the accuracy and reliability of the model.

[0103] S203. Based on the sensor data during the gearbox test process, determine the operating condition data for multiple time periods.

[0104] In some embodiments, the operating condition data includes operating condition characteristics and temperature change characteristics.

[0105] Exemplarily, preprocess the collected sensor data, including denoising, filtering, outlier processing, etc. Extract the operating condition characteristics from the preprocessed data, such as vibration frequency, amplitude, temperature change rate, etc. According to the time line of the test process, divide the sensor data into multiple time periods, and each time period corresponds to a specific operating condition.

[0106] S204. Based on the operating condition characteristics and stress data for multiple time periods, perform Gaussian regression fitting to obtain a Gaussian process regression module.

[0107] Exemplarily, organize the operating condition characteristics and stress data into a training set and a test set. Use the Gaussian process regression algorithm to train the training set to obtain a Gaussian process regression model. Use the test set to verify the trained model and evaluate the prediction performance of the model.

[0108] S205. Based on the temperature change characteristics for multiple time periods and the fault characteristics for multiple time periods, perform neural network training to obtain an artificial intelligence module.

[0109] Exemplarily, select the characteristics useful for fault prediction from the temperature change characteristics and the fault characteristics. Neural network design: Design an appropriate neural network structure, such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc. Model training: Use the temperature change characteristics and the fault characteristics to train the neural network to obtain an artificial intelligence module capable of predicting faults.

[0110] S206. Based on the 3D structure module of the gearbox, the Gaussian process regression module, and the artificial intelligence module, construct a digital twin model.

[0111] Exemplarily, integrate the 3D structure module of the gearbox, the Gaussian process regression module, and the artificial intelligence module together to form a complete digital twin model. Model verification and optimization: Verify and optimize the constructed digital twin model to ensure that the model can accurately simulate the actual operation of the gearbox. Interface design: Design the interface for the digital twin model to interact with the external environment, such as data input / output interfaces, visualization interfaces, etc.

[0112] Thus, the embodiments of the present invention can construct a digital twin model before performing performance detection on the gearbox, facilitating the visual processing of gearbox faults and improving the maintenance efficiency of gearbox fault handling.

[0113] Optionally, the operation and maintenance detection method for the gearbox provided by the embodiments of the present invention further includes steps S301 - S304.

[0114] S301. Record the sensor data of the gearbox at multiple time periods after maintenance.

[0115] Exemplarily, after the gearbox is maintained, plan multiple monitoring time periods according to actual needs. The length of the time period can be flexibly set according to the operating characteristics of the gearbox and the maintenance content. Ensure that the sensors installed on the gearbox (such as vibration sensors, temperature sensors, pressure sensors, etc.) are in normal working condition and can accurately collect the required data. During each monitoring time period, collect the sensor data of the gearbox regularly or continuously to ensure the integrity and continuity of the data. Store the collected sensor data properly for subsequent analysis and processing.

[0116] S302. Determine the working condition data of multiple time periods based on the sensor data of multiple time periods.

[0117] Exemplarily, preprocess the collected sensor data, including data cleaning (removing outliers, filling missing values), data conversion (such as unit conversion, data standardization), etc. Extract the features that can reflect the working condition of the gearbox from the preprocessed sensor data, such as vibration frequency, amplitude, temperature change trend, pressure fluctuation, etc. According to the extracted features, construct the working condition data set of each time period, including various working condition features and corresponding time period identifiers.

[0118] S303. Determine the performance data of the gearbox at multiple time periods based on the working condition data of multiple time periods and the digital twin model.

[0119] Exemplarily, use the working condition data of each time period as input and input it into the pre - constructed digital twin model. Simulate the operating conditions of the gearbox at each time period in the digital twin model, including the interaction of each component, stress distribution, energy transfer, etc. Extract the performance data of the gearbox at each time period from the digital twin model, such as stress, temperature, wear degree, fault risk, etc.

[0120] S304. Evaluate the health status of the gearbox after maintenance based on the performance data of the gearbox at multiple time periods and determine the maintenance effect.

[0121] Exemplarily, embodiments of the present invention can compare the performance data of the gearbox after maintenance in each period with the performance data before maintenance, and analyze the changing trend of the performance data. According to the changing trend of the performance data, evaluate the health status of the gearbox after maintenance, including the degree of overall performance recovery, the reduction of failure risk, etc. Based on the comprehensive evaluation results, determine the effect of the gearbox maintenance, such as whether the expected goal is achieved, whether further maintenance is required, etc. According to the evaluation results, provide feedback and optimization to the digital twin model to improve the accuracy and reliability of the model; at the same time, according to the maintenance effect, adjust the subsequent maintenance plan and strategy.

[0122] In this way, embodiments of the present invention can analyze the sensor data of the gearbox in multiple periods after maintenance and evaluate the maintenance effect.

[0123] In some embodiments, the performance data further includes the failure probability corresponding to the failure type; Optionally, for the operation and maintenance detection method of the gearbox provided by embodiments of the present invention, after step S103, it further includes steps S401 - S406.

[0124] S401: Based on the performance data of the current period and the performance data of multiple time periods within the pre - stored historical period, evaluate the health status of the gearbox and determine the health parameters of the gearbox.

[0125] Exemplarily, embodiments of the present invention can integrate the performance data of the current period (including the failure probability corresponding to the failure type, etc.) with the performance data of multiple time periods within the pre - stored historical period to form a complete data set. This data set will be used to comprehensively reflect the performance of the gearbox at different time periods. Using machine learning or statistical methods, such as support vector machines, random forests, neural networks, etc., construct a health assessment model. This model can output the evaluation result of the health status of the gearbox according to the input performance data. Input the integrated data set into the health assessment model, and the model will output the health parameters of the gearbox. These health parameters can be one or more values, used to quantify the health status of the gearbox. For example, the health parameter can be a value between 0 and 1, where 1 represents that the gearbox is in a completely healthy state and 0 represents that the gearbox is in a serious failure state. In addition to calculating the health parameters of the current period, the changing trend of the health parameters over time can also be analyzed to understand the evolution of the health status of the gearbox.

[0126] S402: Based on the health parameters of the gearbox, determine the maintenance cycles of the components of the gearbox.

[0127] Exemplarily, embodiments of the present invention can decompose the health parameters of the gearbox into the health degrees of its components. This can be achieved by analyzing the performance data of the components inside the gearbox, such as the vibration data of bearings, the wear data of gears, etc. According to the health degrees of the components, combined with information such as the life curves and failure probabilities of the components, the maintenance cycles of the components are calculated. The maintenance cycle can be a time range, indicating that maintenance operations need to be performed on the component within this time range. According to the health degrees and maintenance cycles of the components, the maintenance tasks of the gearbox are prioritized. Components with higher priorities should be maintained first to ensure the overall health and safe operation of the gearbox.

[0128] S403. Determine the maintenance plan of the gearbox based on the maintenance cycles of the components of the gearbox and the work plan of the gearbox.

[0129] Exemplarily, embodiments of the present invention can analyze the work plan of the gearbox, including information such as the running time, downtime, and load conditions of the gearbox. This information will be used to determine the feasibility of the gearbox maintenance plan. According to the maintenance cycles of the components and the work plan, a maintenance plan for the gearbox is formulated. The maintenance plan should include key information such as maintenance time, maintenance content, and maintenance personnel. When formulating the maintenance plan, consider the availability of maintenance resources (such as personnel, equipment, spare parts, etc.), as well as the impact of maintenance on the operation of the gearbox. By optimizing the maintenance plan, ensure that the maintenance tasks can be completed efficiently and orderly, while minimizing the impact on the operation of the gearbox. During the execution of the maintenance plan, monitor and record the maintenance process. This helps to promptly discover potential problems and adjust and optimize the maintenance plan. At the same time, the maintenance records also provide valuable data support for subsequent health assessment and maintenance cycle calculation.

[0130] In this way, embodiments of the present invention can analyze the health status of the gearbox before a failure occurs in the gearbox, and coordinate the maintenance plan of the gearbox, facilitating the overall planning of gearbox maintenance and extending the service life of the gearbox.

[0131] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0132] The following is an apparatus embodiment of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiments above.

[0133] Figure 2 The structural schematic diagram of an operation and maintenance detection device for a gearbox provided by an embodiment of the present invention is shown. The operation and maintenance detection device 500 includes a communication module 501 and a processing module 502.

[0134] A communication module 501, configured to obtain sensor data of the gearbox in the current period; the sensor data includes vibration signals, infrared data, rotational speed, pressure, and load.

[0135] A processing module 502, configured to perform feature extraction and transformation based on the sensor data to obtain the operating condition data in the current period; perform operation and maintenance detection and analysis based on the operating condition data and a preset digital twin model to determine the performance data of the gearbox in the current period; the performance data includes stress data of each node in the gearbox and a fault detection result; the fault detection result includes a fault type, a faulty component, and a fault location; generate a plurality of feasible maintenance plans based on the performance data of the gearbox in the current period; generate and display a visualization interface of the gearbox based on the performance data, the plurality of feasible maintenance plans, and the digital twin model.

[0136] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, the electronic device 600 of this embodiment includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program 603, the steps in the above method embodiments are implemented, such as Figure 1 the steps S101 - S105 shown. Alternatively, when the processor 601 executes the computer program 603, the functions of each module / unit in the above device embodiments are implemented. For example, Figure 2 the functions of the communication module 501 and the processing module 502 shown.

[0137] Exemplarily, the computer program 603 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 602 and executed by the processor 601 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 603 in the electronic device 600. For example, the computer program 603 can be divided into Figure 2 the communication module 501 and the processing module 502 shown.

[0138] The so-called processor 601 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0139] The memory 602 may be an internal storage unit of the electronic device 600, such as the hard disk or memory of the electronic device 600. The memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the electronic device 600. Further, the memory 602 may also include both the internal storage unit of the electronic device 600 and the external storage device. The memory 602 is used to store the computer program and other programs and data required by the terminal. The memory 602 may also be used to temporarily store data that has been output or is to be output.

[0140] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A gearbox operation and maintenance detection method, characterized in that: include: Acquire sensor data of the gearbox in the current period; the sensor data includes vibration signal, infrared data, speed, pressure and load; Based on the sensor data, feature extraction and conversion are performed to obtain the working condition data of the current period; Based on the operating condition data and the preset digital twin model, operation and maintenance detection analysis is performed to determine the performance data of the gearbox in the current period; the performance data includes stress data and fault detection results of each node in the gearbox; The fault detection result includes the fault type, fault component and fault location; Generate multiple feasible maintenance plans based on the performance data of the gearbox in the current period; Based on the performance data, the multiple feasible maintenance solutions, and the digital twin model, a visualization interface of the gearbox is generated and displayed.

2. The gearbox operation and maintenance detection method according to claim 1, characterized in that: Before the operation and maintenance detection analysis is performed based on the operating condition data and the preset digital twin model to determine the performance data of the gearbox, the method further includes: Acquire geometric data of each component of the gearbox, sensor data and stress data during the gearbox test, and fault characteristics of each time period during the gearbox test, wherein the fault characteristics include: fault type, fault component, and fault location; Based on the geometric data of the various components of the gearbox, a three-dimensional structure module of the gearbox is constructed; Based on the sensor data during the gearbox test, determining the operating data of multiple time periods; the operating data includes operating characteristics and temperature change characteristics; Based on the working condition characteristics and stress data of the multiple time periods, Gaussian regression fitting is performed to obtain a Gaussian process regression module; Based on the temperature change characteristics of multiple time periods and the fault characteristics of multiple time periods, neural network training is performed to obtain an artificial intelligence module; The digital twin model is constructed based on the gearbox three-dimensional structure module, Gaussian process regression module, and artificial intelligence module.

3. The gearbox operation and maintenance detection method according to claim 1, characterized in that: Based on the operating condition data and the preset digital twin model, operation and maintenance detection analysis is performed to determine the performance data of the gearbox, including: Based on the working condition characteristics in the working condition data and the Gaussian process regression module in the digital twin model, the stress data of each node in the gearbox is determined; Based on the temperature change characteristics in the operating condition data and the artificial intelligence model in the digital twin model, the fault detection result of the gearbox is determined.

4. The gearbox operation and maintenance detection method according to claim 1, characterized in that: The method generates multiple feasible maintenance plans based on the performance data of the gearbox in the current period, including: Based on the performance data of the current period and the performance data of multiple time periods in the pre-stored historical period, the gearbox is evaluated for performance to determine the performance evaluation level of the gearbox; the performance evaluation level includes normal, minor fault and major fault; Based on the performance evaluation level of the gearbox and the fault detection result, determining the fault information to be maintained, the fault information including the component to be maintained and the fault type; Based on the fault information to be maintained and the maintenance strategy library, multiple pending maintenance plans are determined, wherein the pending maintenance plans include the required maintenance time, maintenance personnel, maintenance tools and spare parts; Determine multiple maintenance times based on the gearbox work plan and the required maintenance time; Based on the multiple maintenance times and the pending maintenance plans, multiple feasible maintenance plans are generated; the feasible maintenance plans include maintenance times, maintenance personnel, maintenance tools and spare parts.

5. The gearbox operation and maintenance detection method according to claim 1, characterized in that: The generating and displaying of a visual interface of the gearbox based on the performance data, the multiple feasible maintenance solutions, and the digital twin model comprises: Based on the stress data in the performance data, the gearbox three-dimensional structure module in the digital twin model is updated and annotated to obtain an updated gearbox three-dimensional structure module; Based on the fault detection results in the performance data and the performance evaluation level, rendering the updated gearbox three-dimensional structure module to obtain a rendered three-dimensional structure module; Based on the rendered three-dimensional structure module, generating a first window; Based on the fault detection result and the performance evaluation level, determining the health status of each component of the gearbox; generating a second window based on the health status of each component of the gearbox; Based on the multiple feasible maintenance solutions, generate a third window in the form of a two-dimensional chart; A visualization interface of the gearbox is generated based on the first window, the second window and the third window.

6. The gearbox operation and maintenance detection method according to claim 5, characterized in that: The updated gearbox three-dimensional structure module is rendered based on the fault detection result in the performance data and the performance evaluation level to obtain the rendered three-dimensional structure module, including: If the fault detection result is a minor fault, and the performance evaluation level is a minor fault, the faulty component is rendered in a first color, and other components except the faulty component are rendered in a second color; If the fault detection result is a serious fault, or the performance evaluation level is a serious fault, the faulty component is rendered in a first color and a second color respectively, and the faulty component is displayed alternately and flashingly in the first window; and other components except the faulty component are rendered in the second color.

7. The gearbox operation and maintenance detection method according to claim 1, characterized in that: The feature extraction and conversion based on the sensor data to obtain the working condition data of the current period includes: Performing wavelet transform processing on the vibration signal of the current period to obtain a two-dimensional time-frequency image; the two-dimensional time-frequency image is used to reflect the time domain information and frequency domain information of the vibration signal; Calculate the statistical information of each sensor data based on the speed, pressure and load of the current period; the statistical information includes the average value, variance, median value, maximum value and minimum value; Based on the data of each sensor, the statistical information of each sensor data, and the two-dimensional time-frequency image, feature fusion is performed to obtain the working condition feature; Based on the infrared data of the current period, finite element analysis is performed to determine the temperature distribution data of the gearbox in the current period; Determine the temperature variation characteristics of the current period based on the temperature distribution data of the current period in the gearbox; Based on the operating condition characteristics and the temperature change characteristics, the operating condition data of the current period is determined.

8. The gearbox operation and maintenance detection method according to claim 1, characterized in that: The method further comprises: Record sensor data of the gearbox at multiple time periods after maintenance; Determine working condition data of multiple time periods based on sensor data of multiple time periods; Determining performance data of the gearbox in multiple time periods based on the operating condition data in multiple time periods and the digital twin model; Based on the performance data of the gearbox in multiple time periods, the health status of the gearbox after maintenance is evaluated to determine the maintenance effect.

9. The gearbox operation and maintenance detection method according to claim 1, characterized in that: The performance data also includes the failure probability corresponding to the failure type; Accordingly, after the operation and maintenance detection analysis is performed based on the operating condition data and the preset digital twin model to determine the performance data of the gearbox in the current period, the method further includes: Based on the performance data of the current period and the pre-stored performance data of multiple time periods in the historical period, the health status of the gearbox is evaluated to determine the health parameters of the gearbox; Determining maintenance periods of various components of the gearbox based on the health parameters of the gearbox; A maintenance plan for the gearbox is determined based on the maintenance cycles of the various components of the gearbox and the work plan of the gearbox.

10. A gearbox operation and maintenance detection device, characterized in that: include: A communication module, used to obtain sensor data of the gearbox in the current period; the sensor data includes vibration signals, infrared data, speed, pressure and load; A processing module, used for performing feature extraction and conversion based on the sensor data to obtain operating condition data of the current period; Based on the operating condition data and the preset digital twin model, operation and maintenance detection analysis is performed to determine the performance data of the gearbox in the current period; the performance data includes stress data and fault detection results of each node in the gearbox; The fault detection result includes the fault type, fault component and fault location; Generate multiple feasible maintenance plans based on the performance data of the gearbox in the current period; Based on the performance data, the multiple feasible maintenance solutions, and the digital twin model, a visualization interface of the gearbox is generated and displayed.

Images