WO2023160300A1 - 结构件剩余寿命预测方法、装置及作业机械 - Google Patents

结构件剩余寿命预测方法、装置及作业机械 Download PDF

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Publication number
WO2023160300A1
WO2023160300A1 PCT/CN2023/072159 CN2023072159W WO2023160300A1 WO 2023160300 A1 WO2023160300 A1 WO 2023160300A1 CN 2023072159 W CN2023072159 W CN 2023072159W WO 2023160300 A1 WO2023160300 A1 WO 2023160300A1
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sample
remaining life
data
predicted
working machine
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PCT/CN2023/072159
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English (en)
French (fr)
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李曾
王佳宇
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上海三一重机股份有限公司
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Publication of WO2023160300A1 publication Critical patent/WO2023160300A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present application relates to the technical field of engineering machinery, and in particular to a method, device and operating machinery for predicting the remaining life of structural parts.
  • Structural parts to be predicted refer to various components that constitute the operating machine entity, such as booms, sticks, and buckets in excavators.
  • the overhaul and maintenance of structural parts to be predicted is of great significance to the normal operation of working machinery. Based on the remaining life of the structural parts to be predicted, more accurate and efficient overhaul and maintenance of the structural parts to be predicted can be performed.
  • the remaining life of the structure to be predicted can be predicted based on the high-frequency vibration data of the structure to be predicted.
  • the accuracy of the remaining life prediction method based on the above-mentioned existing structural part remaining life prediction method is not high.
  • This application provides a method, device, and operating machine for predicting the remaining life of a structural part, which is used to solve the defect of low accuracy in predicting the remaining life of the structural part to be predicted in the prior art, and realize more accurate prediction of the structural part to be predicted remaining lifespan.
  • This application provides a method for predicting the remaining life of structural parts, including:
  • the target data includes: The pressure and the action data of the structure to be predicted; the remaining life prediction model is obtained after training based on the sample data and corresponding labels; the sample data includes the pressure of the hydraulic cylinder in the sample operation machine within the sample period and the action data of the sample structural member in the sample working machine; the label corresponding to the sample data includes the failure information of the sample structural member within the sample period.
  • the sample data further includes: the output power of the engine in the sample machine within the sample period, the output torque of the engine, the At least one of the flow rate of the hydraulic pump, the pressure of the hydraulic pump, and the oil temperature of the hydraulic oil in the sample work machine;
  • the target data also includes: the output power of the engine in the working machine, the output torque of the engine, the flow rate of the hydraulic pump in the working machine, and the pressure of the hydraulic pump within the preset period of time. and at least one of the oil temperature of the hydraulic oil in the working machine; the target data is of the same type as the data included in the sample data.
  • the remaining life prediction model is trained based on the sample data and corresponding labels, specifically including:
  • the sample attenuation sub-model corresponding to the sample structure is obtained; wherein, the sample attenuation sub-model is used to describe the stress and The relationship between remaining life;
  • the remaining life prediction model is trained to obtain a trained remaining life prediction model.
  • the acquisition of target data of an operating machine specifically includes:
  • the data processing includes: data screening and/or feature engineering processing.
  • the target data is input into the remaining life prediction model, so that after the remaining life prediction model outputs the remaining life of the structural part to be predicted in the working machine,
  • the method also includes:
  • the maintenance plan of the structural component to be predicted is obtained.
  • the present application also provides a device for predicting the remaining life of structural parts, including:
  • a data acquisition module configured to acquire target data of the operating machine
  • a life prediction module configured to input the target data into a remaining life prediction model, so that the remaining life prediction model outputs the remaining life of the structure to be predicted in the working machine;
  • the target data includes: the pressure of the hydraulic cylinder in the working machine and the action data of the structure to be predicted within a preset period of time; the remaining life prediction model is trained based on sample data and corresponding labels obtained later; the sample data includes the pressure of the hydraulic cylinder in the sample working machine within the sample period and the action data of the sample structural parts in the sample working machine; the label corresponding to the sample data includes the The fault information of the sample structure is described.
  • the present application also provides an operating machine, including: the device for predicting the remaining life of a structural component as described above.
  • the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. Life Prediction Methods.
  • the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for predicting the remaining life of a structural member as described in any one of the above-mentioned methods is realized.
  • the present application also provides a computer program product, including a computer program.
  • a computer program product including a computer program.
  • the computer program is executed by a processor, the method for predicting the remaining life of a structural member described in any one of the above methods is realized.
  • the remaining service life prediction method, device and working machine of structural parts obtained the target data including the pressure of the hydraulic cylinder in the working machine and the action data of the structural parts to be predicted in the working machine, and input the above target data into the training
  • the remaining life prediction model of the above remaining life prediction model can obtain the remaining life of the structural parts to be predicted outputted by the above remaining life prediction model, which can more accurately predict the remaining life of the structural parts to be predicted, and can detect the abnormality of the structural parts to be predicted in advance, so as to reserve enough It is more reliable, more practical and more universal to predict the remaining life of the structural parts to be predicted by overhauling and maintaining the operating machinery.
  • Fig. 1 is one of the schematic flow charts of the method for predicting the remaining life of structural parts provided by the present application;
  • Fig. 2 is a structural schematic diagram of the remaining life prediction device for structural parts provided by the present application.
  • Fig. 3 is the second schematic flow chart of the method for predicting the remaining life of structural parts provided by the present application.
  • FIG. 4 is a schematic structural diagram of an electronic device provided by the present application.
  • connection should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection , or integrally connected; it may be mechanically connected or electrically connected; it may be directly connected or indirectly connected through an intermediary, and it may be the internal communication of two components.
  • installation should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection , or integrally connected; it may be mechanically connected or electrically connected; it may be directly connected or indirectly connected through an intermediary, and it may be the internal communication of two components.
  • the sample structural parts can be tested under the design conditions to obtain the performance curve used to describe the relationship between the high-frequency vibration data and the remaining life of the sample structural parts. Based on the above performance curve and the high-frequency vibration data of the structural part to be predicted in the working machine, the remaining life of the structural part to be predicted can be obtained.
  • the above structural parts to be predicted and the sample structural parts are the same structural parts to be predicted in the working machines of the same type and model.
  • the traditional method for predicting the remaining life of structural parts has the following defects: First, the above-mentioned performance curves are obtained under design conditions, while the high-frequency vibration data of the structural parts to be predicted in the working machine under actual working conditions are affected by external factors Larger, the above performance curve cannot accurately describe the actual The relationship between the high-frequency vibration data and the remaining life of the above-mentioned structural parts to be predicted under actual working conditions, the accuracy of the remaining life of the above-mentioned structural parts to be predicted based on the above performance curve prediction is not high.
  • the present application provides a method for predicting the remaining life of a structural component. Based on the method for predicting the remaining life of structural parts provided in this application, the remaining life of the structural parts to be predicted can be obtained more accurately, reliably and universally, and based on the obtained remaining life of the structural parts to be predicted, the The maintenance plan of the above-mentioned structural parts to be predicted.
  • Fig. 1 is one of the schematic flowcharts of the method for predicting the remaining life of a structural component provided in the present application.
  • the method for predicting the remaining life of a structural part of the present application will be described below with reference to FIG. 1 .
  • the method includes: step 101 , acquiring target data of the working machine; wherein, the target data includes: the pressure of the hydraulic cylinder in the working machine within a preset period of time and the action data of the structure to be predicted.
  • the remaining life of the structural part to be predicted in the working machine can be predicted, and the remaining life of the structural part to be predicted can be obtained.
  • the working machine is an excavator
  • the structural part to be predicted is the boom in the excavator as an example to illustrate the method for predicting the remaining life of the structural part provided by the present application.
  • An excavator also known as an excavating machine or an excavator, is a construction machine that uses a bucket to dig materials higher or lower than the bearing surface, and loads them into transport vehicles or unloads them to the stockyard.
  • the boom in the excavator is the core predictable structural part of the excavator, and the movement of the stick and bucket in the excavator needs to rely on the stable operation of the boom.
  • the fatigue loss of the boom is accelerated. If the fatigue loss of the boom is excessive, it will cause failures such as cracking of the boom, resulting in remaining parts of the boom. End of life.
  • the target data of the excavator in the embodiment of the present application may include the pressure of the hydraulic cylinder in the excavator and the movement data of the above-mentioned boom within a preset period of time.
  • the target data of the excavator can be obtained in a variety of ways, for example: the original pressure of the hydraulic cylinder in the excavator and the original movement data of the boom within a preset period can be obtained based on big data technology, and directly used as the target data of the excavator; or, It is also possible to obtain the original pressure of the hydraulic cylinder in the excavator and the original movement data of the boom within a preset period of time based on big data technology as the original data of the excavator. The data is used as the target data of the excavator.
  • the preset period of time may be determined according to actual conditions, for example: the preset period of time may be a period of preset duration before the current moment. In the embodiment of the present application, the preset time period is not specifically limited.
  • the preset time may be a period of 100 hours before the current time.
  • the motion data of the boom within the preset time period may include data such as the type and time of each motion performed by the boom within the preset time period, and the magnitude of the above-mentioned motions.
  • the above action data may be action codes.
  • Step 102 Input the target data into the remaining life prediction model, so that the remaining life prediction model outputs the remaining life of the structure to be predicted in the working machine.
  • the remaining life prediction model is obtained after training based on the sample data and corresponding labels;
  • the sample data includes the pressure of the hydraulic cylinder in the sample operation machine within the sample period and the action data of the sample structural parts in the sample operation machine;
  • the sample data The corresponding label includes the failure information of the sample structural member during the sample period.
  • the remaining life prediction model can be trained based on the sample data and corresponding labels. Obtain a trained remaining life prediction model.
  • the sample excavator may be the same as or different from the above-mentioned excavator where the boom to be predicted is located.
  • an excavator of the same model as the excavator on which the boom to be predicted is located and which is in a normal operating state can be used as the sample excavator.
  • the original pressure of the hydraulic cylinder in the sample excavator and the original motion data of the sample boom in the sample operating machine can be obtained based on big data technology as sample data; or, the sample excavator can also be obtained based on big data technology
  • the original pressure of the middle hydraulic cylinder and the original motion data of the sample boom in the sample operation machine are used as sample raw data.
  • the sample raw data after data processing is used as sample data.
  • the normal working state may refer to the working state in which the sample excavator is working in a normal working scene and a normal working condition.
  • sample period may be determined according to actual conditions, for example, the sample period may be a period of preset duration before a certain historical moment. In the embodiment of the present application, the sample period is not specifically limited.
  • the data processing performed on the above sample raw data may include but not limited to data screening and/or feature engineering processing.
  • data screening on the sample raw data above can eliminate invalid data in the sample raw data, for example: sample raw data when the engine in the sample excavator is idling within the sample period.
  • feature engineering processing By performing feature engineering processing on the sample raw data, sample feature data of the sample raw data can be obtained, and the sample feature data can better describe the sample raw data.
  • feature engineering refers to the process of screening out better data features from raw data through a series of engineering methods.
  • Feature engineering process can include feature extraction, feature construction, feature selection, etc.
  • the sample characteristic data of the sample raw data may include but not limited to the maximum value, minimum value, average value, slope and variance of the sample raw data.
  • the fault information of the sample boom within the above sample period can be obtained based on big data technology, and used as the corresponding label of the sample data.
  • the failure information of the sample boom may include information such as the time when the sample boom fails, the type of the failure, and the severity of the failure.
  • the remaining life prediction model can be trained based on the sample data and corresponding labels to obtain a trained remaining life prediction model.
  • the target data of the excavator can be input into the above trained remaining life prediction model, and the remaining life of the boom in the excavator output by the above trained remaining life prediction model can be obtained.
  • the remaining life of the boom can be sent to a display device for display, and a notification message carrying the remaining life of the boom can also be sent to the electronic device used by the user
  • the electronic device used by the above-mentioned user may display the above-mentioned notification message on the display screen of the above-mentioned electronic device, so that the above-mentioned user can know the remaining life of the boom.
  • the above-mentioned users may be excavator drivers and/or maintenance personnel.
  • the above method further includes: updating the remaining life prediction model, and obtaining the updated remaining life prediction model.
  • the sample data and corresponding labels can also be updated based on the actual operating data of the excavator and the failure information of the boom, and can be based on the updated
  • the final sample data and corresponding labels are used to update the remaining life prediction model to obtain the updated remaining life prediction model.
  • the remaining life of the boom in the excavator can be obtained based on the updated remaining life prediction model.
  • the prediction accuracy of the remaining life prediction model can be further improved, and thus the accuracy of the prediction of the remaining life of the structure to be predicted can be further improved.
  • the target data including the pressure of the hydraulic cylinder in the working machine and the action data of the structure to be predicted in the working machine are obtained, and the above target data is input into the trained remaining life prediction model to obtain the above remaining life prediction model
  • the output remaining life of the structural parts to be predicted can predict the remaining life of the structural parts to be predicted more accurately, and can detect abnormalities of the structural parts to be predicted in advance, so as to reserve enough time for overhaul and maintenance of the operating machinery.
  • the prediction of the remaining life of structural parts is more reliable, more practical and more universal.
  • the sample data also includes: the output power of the engine in the sample machine, the output torque of the engine, the flow rate of the hydraulic pump in the sample work machine, At least one of the pressure of the hydraulic pump and the oil temperature of the hydraulic oil in the sample work machine.
  • the target data also includes: at least one of the output power of the engine in the work machine, the output torque of the engine, the flow rate of the hydraulic pump in the work machine, the pressure of the engine, and the oil temperature of the hydraulic oil in the work machine within a preset period of time. type; the target data is the same data type as the sample data.
  • the sample data in the embodiment of the present application may also include the output power of the engine in the sample excavator, the output torque of the engine, and the output torque of the above-mentioned sample excavator within the sample period.
  • the remaining life prediction model is trained, and the trained remaining life prediction model has higher prediction accuracy.
  • the sample data may include the pressure of the hydraulic cylinder in the sample excavator, the output power of the engine in the above sample excavator, the output torque of the engine, the flow rate of the hydraulic pump in the above excavator, the pressure of the hydraulic pump and the above-mentioned All in the oil temperature of the hydraulic oil in the excavator.
  • the target data of the excavator is of the same data type as the above sample data.
  • the above sample data may contain more data types than the above target data.
  • the above sample data includes the pressure of the hydraulic cylinder in the sample excavator, the output power of the engine in the above sample excavator, the output torque of the engine, the flow rate of the hydraulic pump in the above excavator, the pressure of the hydraulic pump and the above excavator in the sample period.
  • the above-mentioned target data may include the pressure of the hydraulic cylinder in the excavator, the output power of the engine in the above-mentioned excavator, the output torque of the engine, the hydraulic pressure in the above-mentioned excavator within a preset period of time.
  • the target data may include the pressure of the hydraulic cylinder in the excavator, the output power and output torque of the engine in the excavator within a preset period of time.
  • the above sample data and the above target data may be acquired based on big data technology. won for the specific process of obtaining the above sample data and the above target data, reference may be made to the contents of the above embodiments, and details will not be repeated in the embodiments of this application.
  • the sample data in the embodiment of the present application also includes the output power of the engine in the sample work machine, the output torque of the engine, the flow rate of the hydraulic pump in the above sample work machine, the pressure of the hydraulic pump, and the oil content of the hydraulic oil in the above sample work machine within the sample period.
  • the above target data includes the output power of the engine in the working machine, the output torque of the engine, the hydraulic pump in the working machine.
  • the residual life prediction model is trained based on the sample data and corresponding labels, specifically including: obtaining the sample attenuation sub-model corresponding to the sample structure based on the design parameters and material science test results of the sample structure; Among them, the sample attenuation sub-model is used to describe the relationship between the stress of the sample structure and the remaining life.
  • the remaining life of the excavator arm is also related to the design parameters of the above-mentioned arm and the results of material science tests. Therefore, when training the remaining life prediction model in the embodiment of the present application, the sample attenuation sub-model corresponding to the sample structural part can be obtained first based on the design parameters of the sample structural part and the material science test results, and then based on the above-mentioned sample attenuation sub-model and The sample data and corresponding labels are used to train the remaining life prediction model to obtain a trained remaining life prediction model.
  • the above-mentioned sample attenuation sub-model can be used to describe the relationship between the force condition of the sample structure and the remaining life.
  • the relationship between the force condition of the above-mentioned sample boom and the remaining life can be obtained through numerical calculation, mathematical statistics, etc., and then it can be generated to describe the above-mentioned A sample decay submodel of the relationship between the force condition and the remaining life of a sample boom.
  • the expression form of the sample attenuation sub-model may include but not limited to a mapping table, a fitting function, a fitting curve, and the like.
  • the remaining life prediction model is trained to obtain a trained remaining life prediction model.
  • the above-mentioned sample attenuation sub-model and the sample data are input to the remaining life prediction model in training, and the predicted remaining life of the boom in the excavator output by the remaining life prediction model in training is obtained.
  • the model parameters of the remaining life prediction model in training can be continuously updated to obtain a trained remaining life prediction model.
  • the sample attenuation sub-model corresponding to the sample structural part is constructed based on the design parameters of the sample structural part and the material science test results, and the residual life prediction model is trained based on the above-mentioned sample attenuation sub-model, sample data and corresponding labels , to obtain the remaining life prediction model with higher prediction accuracy, which can further improve the accuracy of the prediction of the remaining life of the structure to be predicted.
  • acquiring target data of the working machine specifically includes: acquiring the original pressure of the hydraulic cylinder in the working machine and the original motion data of the structural member to be predicted within a preset period of time as the raw data of the working machine.
  • the original pressure of the hydraulic cylinder in the excavator and the original movement data of the boom within a preset period of time can be obtained based on big data technology as the original data of the excavator.
  • the original pressure of the hydraulic cylinder in the excavator, the original output power of the engine in the excavator, the original output torque of the engine, and At least one of the original flow rate of the hydraulic pump, the original pressure of the hydraulic pump, and the original oil temperature of the hydraulic oil in the excavator is used as the original data of the excavator.
  • the raw data after data processing is used as target data; wherein, data processing includes: data screening and/or feature engineering processing.
  • data processing can be performed on the above raw data, and the raw data after data processing can be used as the target data of the excavator.
  • the data processing performed on the above raw data may include data screening and/or feature engineering processing.
  • data screening By performing data screening on the above-mentioned raw data, invalid data in the above-mentioned raw data can be eliminated, for example: data when the engine in the excavator is at idle speed within a preset period of time.
  • feature engineering processing By performing feature engineering processing on the above original data, feature data corresponding to the above original data can be obtained, and the above feature data can better describe the above original data.
  • data processing on the above raw data may include data screening and feature Engineering processing.
  • the pressure of the hydraulic cylinder in the excavator and the action data of the boom are obtained within a preset period of time as the original data of the excavator.
  • the processed original data is used as the working machine
  • the above data processing includes data screening and/or feature engineering processing, which can further improve the prediction of the remaining life of the structure to be predicted by eliminating invalid data in the above original data and/or obtaining the feature data corresponding to the above original data the accuracy rate.
  • the above method further includes: based on the remaining life of the structure to be predicted, Obtain the maintenance plan of the structure to be predicted.
  • a maintenance plan for the boom can also be obtained to provide rationalized suggestions for the maintenance of the boom. For example: in the case that the remaining service life of the above-mentioned boom is less than a preset value, the maintenance plan of the above-mentioned boom may be determined as immediate shutdown for maintenance. Another example: usually the above-mentioned boom needs to be overhauled by specialized maintenance personnel.
  • the remaining life of the above-mentioned boom is not less than the preset value, it can be based on the remaining life of the above-mentioned boom and the location of the above-mentioned excavator and the above-mentioned The distance between the locations of the maintenance personnel, the maintenance scheme of the above-mentioned boom is obtained, the time when the above-mentioned maintenance personnel performs maintenance on the above-mentioned boom is determined, and the like.
  • the severity of the abnormality of the boom or the probability of failure of the boom may also be evaluated based on the remaining life of the boom.
  • the maintenance plan of the structure to be predicted can be obtained, and the working machine can be Predict the remaining life of the structural parts to be predicted, obtain the maintenance plan of the structural parts to be predicted, and provide more reasonable maintenance suggestions for the maintenance of the structural parts to be predicted, which can reduce the maintenance workload, reduce maintenance costs, and improve user perception.
  • Fig. 2 is a structural schematic diagram of the remaining life prediction device for structural parts provided by the present application.
  • the remaining life prediction device for structural parts provided by this application will be described below in conjunction with Figure 2. Laws can be cross-referenced.
  • the device includes: a data acquisition module 201 and a life prediction module 202 .
  • the data acquisition module 201 is configured to acquire the target data of the working machine.
  • the life prediction module 202 is configured to input the target data into the remaining life prediction model, so that the remaining life prediction model outputs the remaining life of the structure to be predicted in the working machine.
  • the target data includes: the pressure of the hydraulic cylinder in the working machine and the action data of the structural parts to be predicted within the preset period; the remaining life prediction model is obtained after training based on the sample data and corresponding labels; the sample data includes The pressure of the hydraulic cylinder in the sample working machine and the action data of the sample structural parts in the sample working machine in the sample period; the label corresponding to the sample data, including the fault information of the sample structural part in the sample period.
  • the data acquisition module 201 is electrically connected to the life prediction module 202 .
  • the data acquisition module 201 can be used to obtain the target data of the excavator in various ways, for example: the original pressure of the hydraulic cylinder in the excavator and the original movement data of the boom can be obtained based on big data technology within a preset period of time, directly as the excavator The target data; or, based on big data technology, the original pressure of the hydraulic cylinder in the excavator and the original movement data of the boom within a preset period can also be obtained as the original data of the excavator. After data processing of the above original data, the The raw data after data processing is used as the target data of the excavator.
  • the life prediction module 202 can be used to input the target data of the excavator into the trained remaining life prediction model, and obtain the remaining life of the boom in the excavator output by the above trained remaining life prediction model.
  • the data acquisition module 201 can also be specifically used to obtain the original pressure of the hydraulic cylinder in the working machine and the original motion data of the structure to be predicted within a preset period of time as the original data of the working machine; for the raw data processing, the The raw data that has undergone data processing is used as target data; wherein, data processing includes: data screening and/or feature engineering processing.
  • the device for predicting the remaining life of structural parts may also include a maintenance plan generating module.
  • the maintenance plan generation module can be used to obtain the maintenance plan of the structural part to be predicted based on the remaining life of the structural part to be predicted.
  • the device for predicting the remaining life of structural parts may also include a cloud model updating module.
  • the cloud model update module can be used to refresh the remaining life prediction model and download and update the remaining life from the cloud in response to the user's input after the user passes the identity verification.
  • the life prediction model can also load the remaining life prediction model to the specified electronic equipment in response to the user's input, so as to adapt to the working conditions of the poor communication conditions in the environment where the operating machine is located; wherein, the above electronic equipment can be used by maintenance personnel Smartphones, laptops, and tablets, etc.
  • the device for predicting the remaining life of structural parts may also include an edge computing module.
  • the edge computing module can be used for the actual deployment and execution of the remaining life prediction model. By performing the modeling of complex parameter relationships, it provides computing power for the calculation of the strength of structural parts, life prediction and abnormal warning.
  • the device for predicting the remaining life of structural parts may also include a maintenance information push module.
  • the maintenance information push module can be used to send the predicted remaining life of the structural parts to the display device for display, and can also be used to send a notification message carrying the remaining life of the above-mentioned structural parts to the designated electronic device; After receiving the above-mentioned notification message, the device may display the above-mentioned notification message on the display screen of the above-mentioned electronic device, so that the user can know the remaining life of the boom.
  • the device for predicting the remaining life of a structural component provided in this application can be deployed on the cloud and/or at the edge.
  • the edge end can be the controller of the working machine, the T-box, and the electronic equipment used by the maintenance personnel.
  • the target data including the pressure of the hydraulic cylinder in the working machine and the action data of the structure to be predicted in the working machine are obtained, and the above target data is input into the trained remaining life prediction model to obtain the above remaining life prediction model
  • the output remaining life of the structural parts to be predicted can predict the remaining life of the structural parts to be predicted more accurately, and can detect abnormalities of the structural parts to be predicted in advance, so as to reserve enough time for overhaul and maintenance of the operating machinery.
  • the prediction of the remaining life of structural parts is more reliable, more practical and more universal.
  • FIG. 3 is the second schematic flow diagram of the method for predicting the remaining life of a structural component provided by the present application.
  • the sample attenuation sub-model corresponding to the sample structure is obtained.
  • the parameter and material science test results obtain the sample decay curve corresponding to the sample structure.
  • the original sample data and corresponding tags are obtained. Perform data screening and/or feature engineering processing on the above original sample data and corresponding labels to obtain sample data and corresponding labels.
  • Model training is performed based on the above sample attenuation curve, sample data and corresponding labels to obtain a trained remaining life prediction model.
  • the trained remaining life prediction model can be deployed on the cloud and on the edge.
  • the original data of the operating machine is obtained. Perform data screening and/or feature engineering processing on the above raw data to obtain the target data of the operating machine.
  • the remaining life of the structure to be predicted in the working machine is predicted, and the remaining life of the structure to be predicted is obtained.
  • a preset value for example: whether the remaining life of the structure to be predicted is less than 200 hours.
  • the operating machine is shut down for maintenance. After the operating machine is shut down for maintenance and comes back online, the cloud continues to record the operating data and fault information of the operating machine in real time.
  • the operating machine will continue to operate, and a maintenance plan for the structural part to be predicted will be generated based on the remaining life of the structural part to be predicted, and the cloud will continue to record the operation of the operating machine in real time data and fault information.
  • an operating machine includes: the device for predicting the remaining life of a structural member as described above.
  • the operating machinery includes the above-mentioned residual life prediction device for structural parts.
  • cloud computing By combining cloud computing and edge computing, it can make full use of the big data stored in the cloud to train a more reliable remaining life prediction model.
  • the remaining life prediction model is efficiently and reliably applied to the specified operating machinery.
  • a remaining life prediction Based on a large amount of historical data, a remaining life prediction based on machine learning and differences in working conditions can be established, integrating physical logic relationships and big data, and the model has higher accuracy. You can use your mobile phone to scan the QR code or face recognition to log in to control the update of the edge end model, which has high security.
  • the maintenance plan with the lowest cost is calculated, and the maintenance workers are automatically provided Engineers push maintenance and structural parts enhancement plans to reduce the risk of component damage and add value to maintenance services, reducing the pressure and cost of factories producing additional structural parts.
  • the structure of the remaining life prediction device for structural parts and the specific steps for predicting the remaining life of the structural parts to be predicted can refer to the contents of the above-mentioned embodiments, and will not be repeated in the embodiments of this application.
  • the working machine may be an excavator.
  • the working machine in the embodiment of the present application acquires the target data including the pressure of the hydraulic cylinder in the working machine and the motion data of the structure to be predicted in the working machine, and inputs the target data into the trained remaining life prediction model to obtain the above-mentioned
  • the remaining life of the above-mentioned structural parts to be predicted output by the remaining life prediction model can more accurately predict the remaining life of the structural parts to be predicted, and can detect abnormalities of the structural parts to be predicted in advance, so as to reserve enough time for maintenance and maintenance of operating machinery Maintenance, the prediction of the remaining life of the structural parts to be predicted has higher reliability, stronger practicability and stronger universality.
  • FIG. 4 illustrates a schematic diagram of the physical structure of an electronic device.
  • the electronic device may include: a processor (processor) 410, a communication interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440, Wherein, the processor 410 , the communication interface 420 , and the memory 430 communicate with each other through the communication bus 440 .
  • processor processor
  • Communication interface Communication interface
  • memory memory
  • the processor 410 may call the logic instructions in the memory 430 to execute the method for predicting the remaining life of the structural member, the method including: acquiring the target data of the working machine; inputting the target data into the remaining life prediction model, so that the remaining life prediction model outputs the working machine
  • the above logic instructions in the memory 430 may be implemented in the form of software function units and be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute various aspects of the application. All or part of the steps of the method described in the examples.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. .
  • the present application also provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can Executing the method for predicting the remaining life of structural parts provided by the above methods, the method includes: obtaining the target data of the working machine; inputting the target data into the remaining life prediction model, so that the remaining life prediction model outputs the remaining life of the structural parts to be predicted in the working machine Life;
  • the target data includes: the pressure of the hydraulic cylinder in the working machine and the action data of the structural parts to be predicted within the preset period; the remaining life prediction model is obtained after training based on the sample data and corresponding labels; the sample data , including the pressure of the hydraulic cylinder in the sample working machine and the action data of the sample structural part in the sample working machine in the sample period; the label corresponding to the sample data includes the fault information of the sample structural part in the sample period.
  • the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to perform the method for predicting the remaining life of a structural member provided by the above-mentioned methods.
  • the method includes: obtaining target data of the working machine; inputting the target data into the remaining life prediction model, so that the remaining life predicting model outputs the remaining life of the structural parts to be predicted in the working machine; wherein, the target data includes: the working machine within a preset period The pressure of the hydraulic cylinder and the action data of the structural parts to be predicted; the remaining life prediction model is obtained after training based on the sample data and corresponding labels; the sample data includes the pressure and sample of the hydraulic cylinder in the sample operating machine within the sample period The action data of the sample structural parts in the working machine; the tags corresponding to the sample data, including the fault information of the sample structural parts within the sample period.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without any creative efforts.
  • each embodiment can be implemented by means of software plus a necessary general-purpose hardware platform. over hardware.
  • the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

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Abstract

本申请提供一种结构件剩余寿命预测方法、装置及作业机械，涉及工程机械技术领域，该方法包括：将作业机械的目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命；其中，目标数据，包括：预设时段内作业机械中液压油缸的压力和待预测结构件的动作数据；剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；样本数据，包括样本时段内样本作业机械中液压油缸的压力和样本作业机械中样本结构件的动作数据；样本数据对应的标签，包括样本时段内样本结构件的故障信息。本申请提供的结构件剩余寿命预测方法、装置及作业机械，能更准确的预测待预测结构件的剩余寿命，预测的可靠性更高、实用性更强。

Description

结构件剩余寿命预测方法、装置及作业机械
相关申请的交叉引用
本申请要求于2022年02月22日提交的申请号为202210162037.9，发明名称为“结构件剩余寿命预测方法、装置及作业机械”的中国专利申请的优先权，其通过引用方式全部并入本文。
技术领域
本申请涉及工程机械技术领域，尤其涉及一种结构件剩余寿命预测方法、装置及作业机械。
背景技术
待预测结构件，指构成作业机械实体的各种构件，例如：挖掘机中的动臂、斗杆和铲斗等。待预测结构件的检修和维护，对于作业机械的正常作业具有重要意义。基于待预测结构件的剩余寿命，可以对待预测结构件进行更准确、更高效的检修和维护。
现有技术中，可以基于待预测结构件的高频振动数据，对上述待预测结构件的剩余寿命进行预测。但是，由于影响待预测结构件的高频振动数据的因素较多，导致基于上述现有的结构件剩余寿命预测方法对待预测结构件的剩余寿命进行预测的准确率不高。
发明内容
本申请提供一种结构件剩余寿命预测方法、装置及作业机械，用以解决现有技术中对待预测结构件的剩余寿命进行预测的准确率不高的缺陷，实现更准确的预测待预测结构件的剩余寿命。
本申请提供一种结构件剩余寿命预测方法，包括：
获取作业机械的目标数据；
将所述目标数据输入剩余寿命预测模型，以使所述剩余寿命预测模型输出所述作业机械中待预测结构件的剩余寿命；
其中，所述目标数据，包括：预设时段内所述作业机械中液压油缸的 压力和所述待预测结构件的动作数据；所述剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；所述样本数据，包括样本时段内样本作业机械中液压油缸的压力和所述样本作业机械中样本结构件的动作数据；所述样本数据对应的标签，包括所述样本时段内所述样本结构件的故障信息。
根据本申请提供的一种结构件剩余寿命预测方法，所述样本数据，还包括：所述样本时段内所述样本机械中发动机的输出功率、所述发动机的输出扭矩、所述样本作业机械中液压泵的流量、所述液压泵的压力以及所述样本作业机械中液压油的油温中的至少一种；
相应地，所述目标数据，还包括：所述预设时段内所述作业机械中发动机的输出功率、所述发动机的输出扭矩、所述作业机械中液压泵的流量、所述液压泵的压力以及所述作业机械中液压油的油温中的至少一种；所述目标数据与所述样本数据包括的数据的类型相同。
根据本申请提供的一种结构件剩余寿命预测方法，基于所述样本数据及对应的标签对所述剩余寿命预测模型进行训练，具体包括：
基于所述样本结构件的设计参数和材料学测试结果，获取所述样本结构件对应的样本衰减子模型；其中，所述样本衰减子模型，用于描述所述样本结构件的受力情况与剩余寿命之间的关系；
基于所述样本衰减子模型和所述样本数据及对应的标签，对所述剩余寿命预测模型进行训练，获得训练好的剩余寿命预测模型。
根据本申请提供的一种结构件剩余寿命预测方法，所述获取作业机械的目标数据，具体包括：
获取所述预设时段内所述作业机械中液压油缸的原始压力和所述待预测结构件的原始动作数据，作为所述作业机械的原始数据；
对所述原始数据数据处理，将经过数据处理的原始数据作为所述目标数据；
其中，所述数据处理，包括：数据筛选和/或特征工程处理。
根据本申请提供的一种结构件剩余寿命预测方法，所述将所述目标数据输入剩余寿命预测模型，以使所述剩余寿命预测模型输出所述作业机械中待预测结构件的剩余寿命之后，所述方法还包括：
基于所述待预测结构件的剩余寿命，获取所述待预测结构件的检修方案。
本申请还提供一种结构件剩余寿命预测装置，包括：
数据获取模块，用于获取作业机械的目标数据；
寿命预测模块，用于将所述目标数据输入剩余寿命预测模型，以使所述剩余寿命预测模型输出所述作业机械中待预测结构件的剩余寿命；
其中，所述目标数据，包括：预设时段内所述作业机械中液压油缸的压力和所述待预测结构件的动作数据；所述剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；所述样本数据，包括样本时段内样本作业机械中液压油缸的压力和所述样本作业机械中样本结构件的动作数据；所述样本数据对应的标签，包括所述样本时段内所述样本结构件的故障信息。
本申请还提供一种作业机械，包括：如上所述的结构件剩余寿命预测装置。
本申请还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述任一种所述结构件剩余寿命预测方法。
本申请还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如上述任一种所述结构件剩余寿命预测方法。
本申请还提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述任一种所述结构件剩余寿命预测方法。
本申请提供的结构件剩余寿命预测方法、装置及作业机械，通过获取包括作业机械中液压油缸的压力和上述作业机械中待预测结构件的动作数据的目标数据，并将上述目标数据输入训练好的剩余寿命预测模型，获取上述剩余寿命预测模型输出的上述待预测结构件的剩余寿命，能更准确的预测待预测结构件的剩余寿命，能提前发现待预测结构件的异常，从而预留足够的时间对作业机械进行检修和维护，对待预测结构件的剩余寿命进行预测的可靠性更高、实用性更强且普适性更强。
附图说明
为了更清楚地说明本申请或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请提供的结构件剩余寿命预测方法的流程示意图之一；
图2是本申请提供的结构件剩余寿命预测装置的结构示意图；
图3是本申请提供的结构件剩余寿命预测方法的流程示意图之二；
图4是本申请提供的电子设备的结构示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请中的附图，对本申请中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
在发明的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本申请中的具体含义。
需要说明的是，传统的结构件剩余寿命预测方法中，可以在设计工况下对样本结构件进行测试，获取用于描述样本结构件的高频振动数据与剩余寿命之间关系的性能曲线。基于上述性能曲线和作业机械中待预测结构件的高频振动数据，可以获取上述待预测结构件的剩余寿命。其中，上述待预测结构件与样本结构件为相同类型、相同型号的作业机械中相同的待预测结构件。
但是，传统的结构件剩余寿命预测方法存在以下缺陷：首先，上述性能曲线是在设计工况下获取的，而实际工况下作业机械中待预测结构件的高频振动数据受外界因素的影响较大，上述性能曲线并不能准确的描述实 际工况下上述待预测结构件的高频振动数据与剩余寿命之间的关系，基于上述性能曲线预测得到的上述待预测结构件的剩余寿命准确率不高。
其次，获取上述性能曲线需要在设计工况下对样本结构件进行大量的测试，投入成本较高，通常仅对造价较高、维修较困难或较重要的少量类型的样本结构件进行测试，获取上述样本结构件对应的性能曲线，对于其他类型的待预测结构件，则难以基于传统的结构件剩余寿命预测方法获取上述其他类型的待预测结构件的剩余寿命，传统的结构件剩余寿命预测方法的普适性不强。
最后，基于上述传统的结构件剩余寿命预测方法对上述待预测结构件的剩余寿命进行预测时，往往在上述待预测结构件的振动频谱出现异常或上述待预测结构件出现明显故障的情况下，才能确定上述待预测结构件出现异常，难以预留足够的时间对上述待预测结构件进行检修或控制，现有的结构件剩余寿命预测方法的实用性较弱。
对此，本申请提供一种结构件剩余寿命预测方法。基于本申请提供的结构件剩余寿命预测方法，可以更准确、更可靠以及普适性更强的获取待预测结构件的剩余寿命，并可以基于获取到的上述待预测结构件的剩余寿命，获得上述待预测结构件的检修方案。
图1是本申请提供的结构件剩余寿命预测方法的流程示意图之一。下面结合图1描述本申请的结构件剩余寿命预测方法。如图1所示，该方法包括：步骤101、获取作业机械的目标数据；其中，目标数据，包括：预设时段内作业机械中液压油缸的压力和待预测结构件的动作数据。
需要说明的是，基于本申请提供的结构件剩余寿命预测方法，可以对作业机械中待预测结构件的剩余寿命进行预测，获取上述待预测结构件的剩余寿命。以下以作业机械为挖掘机，待预测结构件为挖掘机中的动臂为例，说明本申请提供的结构件剩余寿命预测方法。
挖掘机，又称挖掘机械或者挖土机，是用铲斗挖掘高于或低于承机面的物料，并装入运输车辆或卸至堆料场的工程机械。挖掘机中的动臂为挖掘机的核心待预测结构件，挖掘机中斗杆和铲斗的动作均需要依托与动臂的稳定运行。在挖掘机进行高强度挖掘工作的情况下，动臂的疲劳损耗加快，若动臂疲劳损耗过度，会造成动臂出现开裂等故障，导致动臂的剩余 寿命终结。
可以理解的是，挖掘机中动臂的剩余寿命与上述动臂的受力情况相关，挖掘机中液压油缸的压力可以反映上述动臂的受力情况。挖掘机中动臂的剩余寿命还与上述动臂进行的动作相关。因此，本申请实施例中挖掘机的目标数据可以包括预设时段内挖掘机中液压油缸的压力和上述动臂的动作数据。通过获取挖掘机的目标数据，可以基于上述目标数据，对上述动臂的剩余寿命进行预测。
可以通过多种方式获取挖掘机的目标数据，例如：可以基于大数据技术获取预设时段内挖掘机中液压油缸的原始压力和动臂的原始动作数据，直接作为挖掘机的目标数据；或者，还可以基于大数据技术获取预设时段内挖掘机中液压油缸的原始压力和动臂的原始动作数据，作为挖掘机的原始数据，对上述原始数据进行数据处理之后，将经过数据处理后的原始数据作为挖掘机的目标数据。
需要说明的是，预设时段可以是根据实际情况确定的，例如：预设时段可以为当前时刻之前预设时长的时段。本申请实施例中，对预设时段不作具体限定。
可选地，预设时刻可以为当前时刻之前100小时的时段。
需要说明的是，预设时段内动臂的动作数据，可以包括预设时段内动臂进行每一动作的类型、时刻以及上述动作的幅度等数据。上述动作数据可以为动作编码。
步骤102、将目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命。
其中，剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；样本数据，包括样本时段内样本作业机械中液压油缸的压力和样本作业机械中样本结构件的动作数据；样本数据对应的标签，包括样本时段内样本结构件的故障信息。
需要说明的是，将挖掘机的目标数据输入剩余寿命预测模型，获取剩余寿命预测模型输出的挖掘机中动臂的剩余寿命之前，可以基于样本数据及对应的标签对剩余寿命预测模型进行训练，获得训练好的剩余寿命预测模型。
具体地，样本挖掘机可以与上述待预测动臂所在的挖掘机相同或不同。在样本挖掘机与上述待预测动臂所在的挖掘机不同的情况下，可以将与上述待预测动臂所在的挖掘机相同型号且处于正常作业状态的挖掘机作为样本挖掘机。
可以基于大数据技术获取样本时段内样本挖掘机中液压油缸的原始压力和样本作业机械中样本动臂的原始动作数据，作为样本数据；或者，还可以基于大数据技术获取样本时段内样本挖掘机中液压油缸的原始压力和样本作业机械中样本动臂的原始动作数据，作为样本原始数据，对上述样本原始数据进行数据处理之后，将经过数据处理后的样本原始数据作为样本数据。其中，正常作业状态，可以指样本挖掘机在常规的作业场景、常规的工况下进行作业的作业状态。
需要说明的是，样本时段可以是根据实际情况确定的，例如：样本时段可以为某一历史时刻之前预设时长的时段。本申请实施例中，对样本时段不作具体限定。
可选地，对上述样本原始数据进行的数据处理，可以包括但不限于数据筛选和/或特征工程处理等。对上述样本原始数据进行数据筛选，可以剔除样本原始数据中的无效数据，例如：样本时段内样本挖掘机中的发动机处于怠速时的样本原始数据。对上述样本原始数据进行特征工程处理，可以获取上述样本原始数据的样本特征数据，上述样本特征数据可以更好的描述上述样本原始数据。
需要说明的是，特征工程处理，指通过一系列工程化的方式从原始数据中筛选出更好的数据特征的过程。特征工程处理可以包括特征提取、特征构建、特征选择等。样本原始数据的样本特征数据可以包括但不限于样本原始数据的极大值、极小值、平均值、斜率以及方差等。
可以基于大数据技术获取上述样本时段内样本动臂的故障信息，作为样本数据的对应的标签。
需要说明的是，样本动臂的故障信息，可以包括样本动臂发生故障的时刻、故障类型以及故障严重程度等信息。
获取样本数据及对应的标签之后，可以基于样本数据及对应的标签，对剩余寿命预测模型进行训练，获得训练好的剩余寿命预测模型。
获得训练好的剩余寿命预测模型之后，可以将挖掘机的目标数据输入上述训练好的剩余寿命预测模型，获得上述训练好的剩余寿命预测模型输出的挖掘机中动臂的剩余寿命。
可选地，获取挖掘机中动臂的剩余寿命之后，可以将上述动臂的剩余寿命发送至显示设备进行显示，还可以向用户使用的电子设备发送携带有上述动臂的剩余寿命的通知消息，上述用户使用的电子设备接收上述通知消息之后，可以在上述电子设备的显示屏上显示上述通知消息，以使得上述用户可以获知上述动臂的剩余寿命。其中，上述用户可以为挖掘机驾驶人员和/或检修人员。
可选地，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命之后，上述方法还包括：对剩余寿命预测模型进行更新，获取更新后的剩余寿命预测模型。
具体地，获取剩余寿命预测模型输出的挖掘机中动臂的剩余寿命之后，还可以基于挖掘机的实际运行数据以及动臂的故障信息，对样本数据及对应的标签进行更新，并可以基于更新后的样本数据及对应的标签，对剩余寿命预测模型进行更新，获取更新后的剩余寿命预测模型。
获取更新后的剩余寿命预测模型之后，在下一次对挖掘机中动臂的剩余寿命进行预测时，可以基于更新后的剩余寿命预测模型，获取挖掘机中动臂的剩余寿命。通过对剩余寿命预测模型进行更新，获取更新后的剩余寿命预测模型，可以进一步提升剩余寿命预测模型的预测准确率，进而能进一步提高对待预测结构件的剩余寿命进行预测的准确率。
本申请实施例通过获取包括作业机械中液压油缸的压力和上述作业机械中待预测结构件的动作数据的目标数据，并将上述目标数据输入训练好的剩余寿命预测模型，获取上述剩余寿命预测模型输出的上述待预测结构件的剩余寿命，能更准确的预测待预测结构件的剩余寿命，能提前发现待预测结构件的异常，从而预留足够的时间对作业机械进行检修和维护，对待预测结构件的剩余寿命进行预测的可靠性更高、实用性更强且普适性更强。
基于上述各实施例的内容，样本数据，还包括：样本时段内样本机械中发动机的输出功率、发动机的输出扭矩、样本作业机械中液压泵的流量、 液压泵的压力以及样本作业机械中液压油的油温中的至少一种。
相应地，目标数据，还包括：预设时段内作业机械中发动机的输出功率、发动机的输出扭矩、作业机械中液压泵的流量、发动机的压力以及作业机械中液压油的油温中的至少一种；目标数据与样本数据的数据类型相同。
需要说明的是，挖掘机中发动机的输出功率、发动机的输出扭矩、上述挖掘机中液压泵的流量、液压泵的压力以及上述挖掘机中液压油的油温中的部分或全部，也可以在一定程度上述反映上述挖掘机中动臂的受力情况，因此，本申请实施例中的样本数据，还可以包括样本时段内样本挖掘机中发动机的输出功率、发动机的输出扭矩、上述样本挖掘机中液压泵的流量、液压泵的压力以及上述样本挖掘机中液压油的油温中的部分或全部。基于包括上述样本数据的样本数据及对应的标签，对剩余寿命预测模型进行训练，获得的训练好的剩余寿命预测模型具有更高的预测准确率。
优选地，样本数据，可以包括样本时段内样本挖掘机中液压油缸的压力、上述样本挖掘机中发动机的输出功率、发动机的输出扭矩、上述挖掘机中液压泵的流量、液压泵的压力以及上述挖掘机中液压油的油温中的全部。
需要说明的是，挖掘机的目标数据与上述样本数据的数据类型相同。但是，上述样本数据中包含的数据类型可以多于上述目标数据中包含的数据类型。例如：在上述样本数据包括样本时段内样本挖掘机中液压油缸的压力、上述样本挖掘机中发动机的输出功率、发动机的输出扭矩、上述挖掘机中液压泵的流量、液压泵的压力以及上述挖掘机中液压油的油温中的全部的情况下，上述目标数据可以包括预设时段内挖掘机中液压油缸的压力、上述挖掘机中发动机的输出功率、发动机的输出扭矩、上述挖掘机中液压泵的流量、液压泵的压力以及上述挖掘机中液压油的油温中的至少一个；或者，在上述样本数据包括样本时段内样本挖掘机中液压油缸的压力、上述样本挖掘机中发动机的输出功率和输出扭矩的情况下，上述目标数据可以包括预设时段内挖掘机中液压油缸的压力、上述挖掘机中发动机的输出功率和输出扭矩。
可选地，可以基于大数据技术获取上述样本数据和上述目标数据。获 取上述样本数据和上述目标数据的具体过程可以参见上述各实施例的内容，本申请实施例中不再赘述。
本申请实施例中样本数据还包括样本时段内样本作业机械中发动机的输出功率、发动机的输出扭矩、上述样本作业机械中液压泵的流量、液压泵的压力以及上述样本作业机械中液压油的油温中的部分或全部，在样本数据与作业机械的目标数据具有对应关系的情况下，上述目标数据包括预设时段内作业机械中发动机的输出功率、发动机的输出扭矩、上述作业机械中液压泵的流量、液压泵的压力以及上述作业机械中液压油的油温中的部分或全部，能基于上述样本数据训练得到具有更高的预测准确率剩余寿命预测模型，能进一步提高对待预测结构件的剩余寿命进行预测的准确率。
基于上述各实施例的内容，基于样本数据及对应的标签对剩余寿命预测模型进行训练，具体包括：基于样本结构件的设计参数和材料学测试结果，获取样本结构件对应的样本衰减子模型；其中，样本衰减子模型，用于描述样本结构件的受力情况与剩余寿命之间的关系。
可以理解的是，挖掘机动臂的剩余寿命，还与上述动臂的设计参数和材料学测试结果相关。因此，本申请实施例中对剩余寿命预测模型进行训练时，可以首先基于样本结构件的设计参数和材料学测试结果，获取样本结构件对应的样本衰减子模型，再基于上述样本衰减子模型和样本数据及对应的标签对剩余寿命预测模型进行训练，获得训练好的剩余寿命预测模型。其中，上述样本衰减子模型，可以用于描述样本结构件的受力情况与剩余寿命之间的关系。
具体地，基于样本结构件的设计参数和材料学测试结果，可以通过数值计算、数理统计等方式，获取上述样本动臂的受力情况与剩余寿命之间的关系，进而可以生成用于描述上述样本动臂的受力情况与剩余寿命之间的关系的样本衰减子模型。
可选地，上述样本衰减子模型的表现形式可以包括但不限于映射表、拟合函数以及拟合曲线等。
基于样本衰减子模型和样本数据及对应的标签，对剩余寿命预测模型进行训练，获得训练好的剩余寿命预测模型。
具体地，获取上述样本动臂对应的样本衰减子模型之后，可以将上述 样本衰减子模型和样本数据输入训练中的剩余寿命预测模型，获取训练中的剩余寿命预测模型输出的挖掘机中动臂的预测剩余寿命。
基于上述挖掘机中动臂的预测剩余寿命和样本数据对应的标签，可以对训练中的剩余寿命预测模型的模型参数进行不断更新，进而获得训练好的剩余寿命预测模型。
本申请实施例通过基于样本结构件的设计参数和材料学测试结果，构建样本结构件对应的样本衰减子模型，基于上述样本衰减子模型和样本数据及对应的标签，对剩余寿命预测模型进行训练，获得具有更高的预测准确率剩余寿命预测模型，能进一步提高对待预测结构件的剩余寿命进行预测的准确率。
基于上述各实施例的内容，获取作业机械的目标数据，具体包括：获取预设时段内作业机械中液压油缸的原始压力和待预测结构件的原始动作数据，作为作业机械的原始数据。
具体地，可以基于大数据技术获取预设时段内挖掘机中液压油缸的原始压力和动臂的原始动作数据，作为挖掘机的原始数据。
需要说明的是，本申请实施例中还可以基于大数据技术获取预设时段内挖掘机中液压油缸的原始压力、上述挖掘机中发动机的原始输出功率、发动机的原始输出扭矩、上述挖掘机中液压泵的原始流量、液压泵的原始压力以及上述挖掘机中液压油的原始油温中的至少一个，作为挖掘机的原始数据。
对原始数据数据处理，将经过数据处理的原始数据作为目标数据；其中，数据处理，包括：数据筛选和/或特征工程处理。
获取挖掘机的原始数据之后，可以对上述原始数据进行数据处理，将经过数据处理后的原始数据作为挖掘机的目标数据。
可选地，对上述原始数据进行的数据处理，可以包括数据筛选和/或特征工程处理。对上述原始数据进行数据筛选，可以剔除上述原始数据中的无效数据，例如：预设时段内挖掘机中的发动机处于怠速时的数据。对上述原始数据进行特征工程处理，可以获取上述原始数据对应的特征数据，上述特征数据可以更好的描述上述原始数据。
优选地，对上述原始数据进行的数据处理，可以包括数据筛选和特征 工程处理。
本申请实施例通过获取预设时段内挖掘机中液压油缸的压力和动臂的动作数据，作为挖掘机的原始数据，对上述原始数据进行数据处理之后，将经过数据处理的原始数据作为作业机械的目标数据，上述数据处理包括数据筛选和/或特征工程处理，能通过剔除上述原始数据中的无效数据和/或获取上述原始数据对应的特征数据，进一步提高对待预测结构件的剩余寿命进行预测的准确率。
基于上述各实施例的内容，将目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命之后，上述方法还包括：基于待预测结构件的剩余寿命，获取待预测结构件的检修方案。
具体地，获取剩余寿命预测模型输出的挖掘机中动臂的剩余寿命之后，基于上述动臂的剩余寿命，还可以获取上述动臂的检修方案，为上述动臂的检修提供合理化建议。例如：在上述动臂的剩余寿命小于预设值的情况下，可以将上述动臂的检修方案确定为立即停机检修。又例如：通常情况下上述动臂需要由专门的检修人员进行上门检修，在上述动臂的剩余寿命不小于预设值的情况下，可以基于上述动臂的剩余寿命以及上述挖掘机所在地与上述检修人员的所在地之间的距离，获取上述动臂的检修方案，确定上述检修人员对上述动臂进行检修的时间等。
可选地，获取剩余寿命预测模型输出的挖掘机中动臂的剩余寿命之后，还可以基于上述动臂的剩余寿命，评估上述动臂出现异常的严重程度或者上述动臂出现故障的概率。
本申请实施例通过在获取剩余寿命预测模型输出的作业机械中待预测结构件的剩余寿命之后，基于上述待预测结构件的剩余寿命，获取上述待预测结构件的检修方案，能通过对作业机械中待预测结构件的剩余寿命的预测，获取上述待预测结构件的检修方案，为上述待预测结构件的检修提供更合理的检修建议，能减少检修工作量，能降低维护成本，能提高用户感知。
图2是本申请提供的结构件剩余寿命预测装置的结构示意图。下面结合图2对本申请提供的结构件剩余寿命预测装置进行描述，下文描述的结构件剩余寿命预测装置与上文描述的本申请提供的结构件剩余寿命预测方 法可相互对应参照。如图2所示，该装置包括：数据获取模块201和寿命预测模块202。
数据获取模块201，用于获取作业机械的目标数据。
寿命预测模块202，用于将目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命。
其中，目标数据，包括：预设时段内作业机械中液压油缸的压力和待预测结构件的动作数据；剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；样本数据，包括样本时段内样本作业机械中液压油缸的压力和样本作业机械中样本结构件的动作数据；样本数据对应的标签，包括样本时段内样本结构件的故障信息。
具体地，数据获取模块201和寿命预测模块202电连接。
数据获取模块201可以用于通过多种方式获取挖掘机的目标数据，例如：可以基于大数据技术获取预设时段内挖掘机中液压油缸的原始压力和动臂的原始动作数据，直接作为挖掘机的目标数据；或者，还可以基于大数据技术获取预设时段内挖掘机中液压油缸的原始压力和动臂的原始动作数据，作为挖掘机的原始数据，对上述原始数据进行数据处理之后，将经过数据处理后的原始数据作为挖掘机的目标数据。
寿命预测模块202可以用于将挖掘机的目标数据输入训练好的剩余寿命预测模型，获得上述训练好的剩余寿命预测模型输出的挖掘机中动臂的剩余寿命。
可选地，数据获取模块201还可以具体用于获取预设时段内作业机械中液压油缸的原始压力和待预测结构件的原始动作数据，作为作业机械的原始数据；对原始数据数据处理，将经过数据处理的原始数据作为目标数据；其中，数据处理，包括：数据筛选和/或特征工程处理。
可选地，结构件剩余寿命预测装置还可以包括检修方案生成模块。
检修方案生成模块可以用于基于待预测结构件的剩余寿命，获取待预测结构件的检修方案。
可选地，结构件剩余寿命预测装置还可以包括云端模型更新模块。
云端模型更新模块，可以用于在用户通过身份验证之后，响应于用户的输入，通过T-box一键刷新剩余寿命预测模型和从云端下载、更新剩余 寿命预测模型，还可以响应于用户的输入，将剩余寿命预测模型加载至指定的电子设备，从而适应作业机械所在环境通信条件较差的工况；其中，上述电子设备，可以为检修人员使用的智能手机、笔记本电脑以及平板电脑等。
可选地，结构件剩余寿命预测装置还可以包括边缘计算模块。
边缘计算模块，可以用于剩余寿命预测模型的实际部署和执行，通过执行对复杂参数关系的建模，为结构件使用强度计算、寿命预测和异常预警提供计算能力。
可选地，结构件剩余寿命预测装置还可以包括检修信息推送模块。
检修信息推送模块，可以用于将预测得到的结构件的剩余寿命发送至显示设备进行显示，还可以用于向指定电子设备发送携带有上述结构件的剩余寿命的通知消息；其中，上述指定电子设备接收上述通知消息之后，可以在上述电子设备的显示屏上显示上述通知消息，以使得用户可以获知上述动臂的剩余寿命。
需要说明的是，本申请提供的结构件剩余寿命预测装置可以部署于云端和/或边缘端。其中，边缘端可以为作业机械的控制器、T-box以及检修人员所使用的电子设备等。
本申请实施例通过获取包括作业机械中液压油缸的压力和上述作业机械中待预测结构件的动作数据的目标数据，并将上述目标数据输入训练好的剩余寿命预测模型，获取上述剩余寿命预测模型输出的上述待预测结构件的剩余寿命，能更准确的预测待预测结构件的剩余寿命，能提前发现待预测结构件的异常，从而预留足够的时间对作业机械进行检修和维护，对待预测结构件的剩余寿命进行预测的可靠性更高、实用性更强且普适性更强。
为了便于对本申请提供的结构件剩余寿命预测方法及装置的理解，以下通过一个实例说明本申请提供的结构件剩余寿命预测方法及装置。图3是本申请提供的结构件剩余寿命预测方法的流程示意图之二。如图3所示，基于样本结构件的设计参数和材料学测试结果，获取样本结构件对应的样本衰减子模型，上述样本衰减子模型的表现形式为拟合曲线，即基于样本结构件的设计参数和材料学测试结果获取样本结构件对应的样本衰减曲线。
基于云端实时记录的作业机械的运行数据和故障信息，获取原始样本数据及对应的标签。对上述原始样本数据及对应的标签进行数据筛选和/特征工程处理，获得样本数据及对应的标签。
基于上述样本衰减曲线和样本数据及对应的标签进行模型训练，获得训练好的剩余寿命预测模型。训练好的剩余寿命预测模型可以进行云端和边缘的部署。
基于云端实时记录的作业机械的运行数据，获取作业机械的原始数据。对上述原始数据进行数据筛选和/特征工程处理，获得作业机械的目标数据。
基于上述目标数据和训练好的剩余寿命预测模型，对作业机械中待预测结构件的剩余寿命进行预测，获得上述待预测结构件的剩余寿命。
判断上述待预测结构件的剩余寿命是否小于预设值，例如：上述待预测结构件的剩余寿命是否小于200小时。
若上述待预测结构件的剩余寿命小于预设值，则对作业机械进行停机检修。在作业机械完成停机检修重新上线之后，云端继续实时记录作业机械的运行数据和故障信息。
若上述待预测结构件的剩余寿命不小于预设值，则作业机械继续进行作业，并基于上述待预测结构件的剩余寿命生成上述待预测结构件的检修方案，云端继续实时记录作业机械的运行数据和故障信息。
基于上述各实施例的内容，一种作业机械，包括：如上述所述的结构件剩余寿命预测装置。
具体地，作业机械包括上述结构件剩余寿命预测装置，可以通过将云计算和边缘计算相结合的方式，充分利用了云端存储的大数据训练更可靠的剩余寿命预测模型，同时利用边缘计算保障了剩余寿命预测模型高效可靠的应用至指定作业机械。可以基于大量历史数据建立起基于机器学习和工况差异的剩余寿命预测，整合了物理逻辑关系和大数据，模型精度更高。可以利用手机扫描二维码或者人脸识别登录控制边缘端模型更新，安全性高，也可以用手机为挖掘机加注模型，解决了特殊工况下如隧道和矿井下挖掘机模型更新难的问题，特别是解决了隧道等维保进场困难、排程较为复杂的场景下的维修保养难题。以异常或故障风险程度和挖掘机与维保基地的时空分布作为决策依据，计算出成本最低的维保方案，自动为维保工 程师推送维保和结构件增强计划，减少部件破损风险并且为维保服务增加附加值，降低工厂生产额外的结构件备件的压力和成本。
上述结构件剩余寿命预测装置的结构及对待预测结构件进行剩余寿命预测的具体步骤可以参见上述各实施例的内容，本申请实施例中不作赘述。
可选地，作业机械可以为挖掘机。
本申请实施例中的作业机械通过获取包括作业机械中液压油缸的压力和上述作业机械中待预测结构件的动作数据的目标数据，并将上述目标数据输入训练好的剩余寿命预测模型，获取上述剩余寿命预测模型输出的上述待预测结构件的剩余寿命，能更准确的预测待预测结构件的剩余寿命，能提前发现待预测结构件的异常，从而预留足够的时间对作业机械进行检修和维护，对待预测结构件的剩余寿命进行预测的可靠性更高、实用性更强且普适性更强。
图4示例了一种电子设备的实体结构示意图，如图4所示，该电子设备可以包括：处理器(processor)410、通信接口(Communications Interface)420、存储器(memory)430和通信总线440，其中，处理器410，通信接口420，存储器430通过通信总线440完成相互间的通信。处理器410可以调用存储器430中的逻辑指令，以执行结构件剩余寿命预测方法，该方法包括：获取作业机械的目标数据；将目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命；其中，目标数据，包括：预设时段内作业机械中液压油缸的压力和待预测结构件的动作数据；剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；样本数据，包括样本时段内样本作业机械中液压油缸的压力和样本作业机械中样本结构件的动作数据；样本数据对应的标签，包括样本时段内样本结构件的故障信息。
此外，上述的存储器430中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个 实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
另一方面，本申请还提供一种计算机程序产品，所述计算机程序产品包括计算机程序，计算机程序可存储在非暂态计算机可读存储介质上，所述计算机程序被处理器执行时，计算机能够执行上述各方法所提供的结构件剩余寿命预测方法，该方法包括：获取作业机械的目标数据；将目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命；其中，目标数据，包括：预设时段内作业机械中液压油缸的压力和待预测结构件的动作数据；剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；样本数据，包括样本时段内样本作业机械中液压油缸的压力和样本作业机械中样本结构件的动作数据；样本数据对应的标签，包括样本时段内样本结构件的故障信息。
又一方面，本申请还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现以执行上述各方法提供的结构件剩余寿命预测方法，该方法包括：获取作业机械的目标数据；将目标数据输入剩余寿命预测模型，以使剩余寿命预测模型输出作业机械中待预测结构件的剩余寿命；其中，目标数据，包括：预设时段内作业机械中液压油缸的压力和待预测结构件的动作数据；剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；样本数据，包括样本时段内样本作业机械中液压油缸的压力和样本作业机械中样本结构件的动作数据；样本数据对应的标签，包括样本时段内样本结构件的故障信息。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通 过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (10)

  1. 一种结构件剩余寿命预测方法，包括：
    获取作业机械的目标数据；
    将所述目标数据输入剩余寿命预测模型，以使所述剩余寿命预测模型输出所述作业机械中待预测结构件的剩余寿命；
    其中，所述目标数据，包括：预设时段内所述作业机械中液压油缸的压力和所述待预测结构件的动作数据；所述剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；所述样本数据，包括样本时段内样本作业机械中液压油缸的压力和所述样本作业机械中样本结构件的动作数据；所述样本数据对应的标签，包括所述样本时段内所述样本结构件的故障信息。
  2. 根据权利要求1所述的结构件剩余寿命预测方法，其中，所述样本数据，还包括：所述样本时段内所述样本机械中发动机的输出功率、所述发动机的输出扭矩、所述样本作业机械中液压泵的流量、所述液压泵的压力以及所述样本作业机械中液压油的油温中的至少一种；
    相应地，所述目标数据，还包括：所述预设时段内所述作业机械中发动机的输出功率、所述发动机的输出扭矩、所述作业机械中液压泵的流量、所述液压泵的压力以及所述作业机械中液压油的油温中的至少一种；所述目标数据与所述样本数据包括的数据的类型相同。
  3. 根据权利要求1或2所述的结构件剩余寿命预测方法，其中，基于所述样本数据及对应的标签对剩余寿命预测模型进行训练，具体包括：
    基于所述样本结构件的设计参数和材料学测试结果，获取所述样本结构件对应的样本衰减子模型；其中，所述样本衰减子模型，用于描述所述样本结构件的受力情况与剩余寿命之间的关系；
    基于所述样本衰减子模型和所述样本数据及对应的标签，对所述剩余寿命预测模型进行训练，获得训练好的剩余寿命预测模型。
  4. 根据权利要求1所述的结构件剩余寿命预测方法，其中，所述获取作业机械的目标数据，具体包括：
    获取所述预设时段内所述作业机械中液压油缸的原始压力和所述待预测结构件的原始动作数据，作为所述作业机械的原始数据；
    对所述原始数据进行数据处理，将经过数据处理的原始数据作为所述目标数据；
    其中，所述数据处理，包括：数据筛选和/或特征工程处理。
  5. 根据权利要求1所述的结构件剩余寿命预测方法，其中，所述将所述目标数据输入剩余寿命预测模型，以使所述剩余寿命预测模型输出所述作业机械中待预测结构件的剩余寿命之后，所述方法还包括：
    基于所述待预测结构件的剩余寿命，获取所述待预测结构件的检修方案。
  6. 一种结构件剩余寿命预测装置，包括：
    数据获取模块，用于获取作业机械的目标数据；
    寿命预测模块，用于将所述目标数据输入剩余寿命预测模型，以使所述剩余寿命预测模型输出所述作业机械中待预测结构件的剩余寿命；
    其中，所述目标数据，包括：预设时段内所述作业机械中液压油缸的压力和所述待预测结构件的动作数据；所述剩余寿命预测模型，是基于样本数据及对应的标签进行训练后得到的；所述样本数据，包括样本时段内样本作业机械中液压油缸的压力和所述样本作业机械中样本结构件的动作数据；所述样本数据对应的标签，包括所述样本时段内所述样本结构件的故障信息。
  7. 一种作业机械，包括：如权利要求6所述的结构件剩余寿命预测装置。
  8. 一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其中，所述处理器执行所述程序时实现如权利要求1至5任一项所述结构件剩余寿命预测方法。
  9. 一种非暂态计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1至5任一项所述结构件剩余寿命预测方法。
  10. 一种计算机程序产品，包括计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1至5任一项所述结构件剩余寿命预测方法。
PCT/CN2023/072159 2022-02-22 2023-01-13 结构件剩余寿命预测方法、装置及作业机械 WO2023160300A1 (zh)

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