CN112163325A - RV reducer service life prediction method based on digital twinning - Google Patents
RV reducer service life prediction method based on digital twinning Download PDFInfo
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Abstract
The service life prediction method of the RV reducer based on the digital twin comprises the following steps: (1) establishing a data acquisition system: using a sensor to acquire data of working conditions and environmental parameters of the RV reducer during actual working; (2) establishing a digital twin model of the RV reducer: establishing a digital twin model of the RV reducer by establishing each sub-model of the digital twin model, and ensuring that real-time data is comprehensively analyzed; (3) predicting the service life of the RV reducer: the sub-model can obtain the predicted service life of the RV reducer through simulation; (4) dynamic real-time monitoring and feedback: and the actual running condition of the RV reducer is monitored through real-time data acquisition, and the reliability of twin model prediction is ensured. According to the method, the virtual simulation technology and the agent model are combined to research the use of the RV reducer, so that the service life of the RV reducer can be quickly and accurately predicted, an entity prototype test is not required, the research cost and time are saved, and the research efficiency is greatly improved.
Description
Technical Field
The invention relates to the field of mechanical life prediction and the specific application field of digital twinning.
Background
The RV (Rotary vector) transmission is a novel transmission mode developed on the basis of cycloidal pin wheel transmission, the RV reducer is a precision reducer with high precision, the structure of the RV reducer is a two-stage crank type closed differential gear train, the RV reducer is widely applied to numerical control machines and aerospace, and particularly to the high-tech fields of industrial robot industry and the like, and the RV reducer has the advantages of small volume, light weight, compact structure, large transmission ratio range, large bearing capacity, high motion precision, high transmission efficiency and the like.
The service life of the RV reducer is an important performance parameter, the service life of the RV reducer is the most obvious and largest place in China and abroad at present, the accuracy of 5000 or even ten thousand hours can be guaranteed by the Nabostadac for the 40E reducer, the performance of each aspect can be obviously reduced only by about 1000 hours for a product which is completely manufactured in China. At present, the domestic RV reducer has a large gap compared with foreign countries, and mainly shows the aspects of short service life, poor transmission precision and the like. Therefore, the reliability life is one of the key factors restricting the development of domestic robots.
The RV reducer is very complex in structure, making prediction of its service life very difficult. The existing prediction methods for the RV reducer are divided into the following 3 types:
the above 3 methods all have certain limitations, the first method can cause the actual service life to have a larger error with the predicted service life, the second method has an overlong period, and the third method integrates the characteristics of the former two methods, can relatively quickly and accurately obtain the predicted actual service life of the RV reducer, but at the same time of high cost, cannot give consideration to the interference of various external factors on the actual use of the RV reducer, and has defects.
The life of an RV reducer is related to the fatigue characteristics and thermal behavior of its parts. Under constant contact load, parts of the RV reducer can experience fatigue failure as the work progresses. Fatigue failure of parts can affect the overall life of the RV reducer. The parts of the RV reducer comprise an input shaft, a planet wheel, a sun wheel, a crank shaft, a needle bearing, a cycloidal gear, a needle gear and the like, wherein the planet wheel, the cycloidal gear and the needle gear are key parts causing the RV reducer to fail, and the crank shaft is the second (YaoLiangjiang, Weizhui, Wanghailong, the reliability analysis of the RV reducer based on FTA and FMEA [ J ] modern manufacturing engineering, 2018(01): 136-. It can be said that the fatigue life of the critical components determines the overall service life of the RV reducer.
Through research, the RV reducer planetary gear and cycloid gear failure modes and reasons are as follows:
for fatigue characteristic analysis of mechanical parts, geometric information, a load spectrum, an S-N curve of a material and a stress distribution rule of the parts of an object model need to be obtained firstly (Zhanghong. RV reducer dynamics modeling and fatigue optimization analysis [ D ]. Nanjing aerospace university, 2019.).
The problem that the theory is separated from reality exists in the conventional RV reducer prediction technology, for predicting the service life of the RV reducer, the method 1 does not consider manufacturing errors, does not analyze the service life of the RV reducer in combination with the actual working condition of the RV reducer, only uses theoretical parameters to simulate to obtain a prediction result, and the accuracy of the prediction result is low; the method 2 and the method 3 only obtain a prediction result through field feedback or experimental data, are long in time and high in consumption in the face of various RV reducers, and cannot meet actual production requirements. The prediction methods do not well integrate multi-source data to obtain more accurate prediction data, the service life prediction research of the RV reducer can be expanded to the fatigue life research of the RV reducer under the condition of matching of parts, relevant service life parameters are solved through a simulation method, a model is used for prediction, and the more accurate service life prediction value is obtained through real-time updating of the data.
The digital twin technology is a technical means integrating multiple physics, multiple scales and multiple subject characteristics, the problem of physical and virtual separation can be well solved by establishing a virtual model with high fidelity and real-time synchronization characteristics to simulate, simulate and feed back the situation of a physical entity in real time, and the accurate prediction effect (ceramic fly, Liu Wei, Liu inspection, Liu Xiao Jun, Liu Qiang, Liu Jiang, Qu, Hu Liang, Zhang Jian nan, Qufeng, Xuwen Jun, Wang Jun, Zhang Feng, Liu Zheng Yu, Lihao, Chengjiang Peak, Qilin, Zhang Heng, Suiyang, He Liang, Yiwang Ming, Chenghui, digital twin and application exploration [ J ]. computer integrated manufacturing system, 2018,24(01): 1-18.).
The invention provides a prediction method of the service life of an RV reducer based on digital twinning, which is characterized in that the actual use data of the RV reducer is collected by using a sensor technology, a digital twinning model of the RV reducer is established according to the size of 1:1, effective information is processed by using a wavelet transformation method, and the processed effective information is input into each submodel in the digital twinning model, wherein the submodel comprises a geometric model, a dynamic model, a thermal model and a stress distribution model. The digital twin model obtains the operation data of the next time period through dynamic simulation, and the data obtains the fatigue life of the RV reducer through fatigue simulation, so that the service life of the RV reducer can be rapidly predicted. The simulated input and output data can predict the fatigue life of the key part of the RV reducer by using the Kriging agent model, the precision of the model is improved by continuously inputting the data, meanwhile, the reliability of the simulated result can be checked by collecting the RV reducer data of the next time period, and the service life prediction accuracy of the RV reducer can be gradually improved by continuously improving the model. The RV reducer parameters are optimally designed by performing comparative analysis on fatigue models generated by digital twin models of RV reducers with different parameters and combining with the use of a particle swarm algorithm to obtain the RV reducer parameter combination for prolonging the service life, so that a basis is provided for optimization of the RV reducer.
Disclosure of Invention
The invention aims to introduce a digital twinning technology into the prediction of the service life of the RV reducer, improve the defects of the conventional RV reducer service life prediction method, and realize more accurate service life prediction of the RV reducer by combining the digital twinning technology.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
the service life prediction method of the RV reducer based on the digital twin comprises the following steps:
(1) establishing a data acquisition system: the method comprises the following steps that data collection is carried out on working conditions and environmental parameters of the RV reducer during actual working by using a sensor, wherein the data collection comprises a torque sensor, a vibration sensor, a temperature sensor and the like, and the collected content comprises data such as geometrical shapes of parts of the RV reducer, material properties, operating environmental parameters, torque, return difference, disturbance and the like;
(2) establishing a digital twin model of the RV reducer: the digital twin model of the RV reducer is established by establishing each sub-model of the digital twin model, so that the real-time data can be comprehensively analyzed. The submodels of the digital twin model comprise a geometric model, a dynamic model, a thermal model and a stress distribution model. And acquiring real-time data through a data acquisition system, and uploading the data to the twin model. Processing the uploaded data, and performing analog simulation in each sub-model;
(3) predicting the service life of the RV reducer: the sub-model can obtain the predicted service life of the RV reducer through simulation. Through recording the prediction conditions of the input parameters and the output life values of the RV reducer, after enough times of operation, a kriging proxy model can be used for replacing a simulation model to predict the life. According to the data prediction of the sub-model operation and the actual operation, the data are updated to each sub-model of the digital twin model in time through the updating iteration of the data, and the accurate service life value of the RV reducer is obtained;
(4) dynamic real-time monitoring and feedback: the actual operation condition of the RV reducer is monitored through real-time data acquisition, and the reliability of twin model prediction can be guaranteed. The actual running condition of the RV reducer can be synchronized with the twin model and the physical model in a data mode, and the simulation data and the actual data are integrated through one-time simulation, so that the running condition of the RV reducer is accurately monitored and predicted. By applying the particle swarm algorithm, the optimized RV reducer parameter combination can be obtained, the service life of the RV reducer can be prolonged, and a data basis is provided for the design of the RV reducer.
Further, the step (1) of establishing the data acquisition system comprises the following processes:
(11) and a sensor is additionally arranged in the RV reducer and used for recording dynamic data of the RV reducer in real time when the RV reducer is used. The sensors include the following categories and uses:
(12) in the actual use of the RV reducer, signals generated by each sensor are converted into a spectrogram by extracting useful information through a wavelet transformation technology;
compared with Fourier transform, the time of each component can be obtained by using wavelet transform to receive signals, the condition of the frequency of the signals changing along with the time, the instantaneous frequency and the amplitude of the instantaneous frequency at each moment can be known, and the instantaneous frequency can be analyzed. When the signal has sudden change, the wavelet transformation can accurately express the change and reflect the change to a time frequency spectrum, so that the failure condition of each part of the RV reducer can be conveniently found.
(13) Uploading data and storing the data in a classified manner;
the step (2) of establishing the RV reducer digital twin model comprises the following processes:
the digital twin model comprises a plurality of submodels, namely a geometric model, a dynamic model, a thermal model, a stress analysis model and a fatigue model which have the same size with the physical model part;
(21) establishing a geometric model and a dynamic model of the RV reducer: modeling 1:1 is carried out on the RV reducer by using modeling software, so that the RV reducer can form mapping with an RV reducer entity, and simulation calculation is carried out better;
the step (22) of establishing the geometric model and the dynamic model comprises the following processes:
(211) collecting and uploading real-time data;
(212) drawing a 3D model of the RV reducer according to the real-time data 1:1 by using SolidWorks software to form a geometric model;
wherein, the parameter equation of the tooth profile of the cycloidal gear is as follows:
K1-short amplitude factor;
e-eccentricity of the cycloid wheel;
iH-cycloidal-pin gear transmission ratio;
rp-the centre circle radius of the pin teeth;
rrp-the needle tooth radius;
the data are actually measured by the sensor.
(213) Guiding key parts of the RV reducer in the geometric model into ANYSS, selecting units, defining materials, dividing a network, and defining external nodes and rigid areas;
(214) and importing the geometric model into ADAMS software, replacing part of flexible bodies of key parts, defining the cycloidal gear, the pin gear, the crank shaft and the planet carrier into the flexible bodies, setting the rest parts as rigid bodies, and adding constraint to form the dynamic model.
And (3) modeling the SolidWorks and storing the SolidWorks into an intermediate format file: x _ t, the parasolid format, was then introduced into ADAMS.
The material properties of each component are determined by the actual RV reducer material, taking the model of RV-40E as an example, the material property diagram of each component is shown in the following table:
the constraints for adding the components are shown in the following table:
(215) performing dynamic simulation according to data such as input torque, return difference and the like of the RV reducer measured in real time;
(216) and outputting the load spectrum of the output shaft of the RV reducer. Necessary data such as real-time data, simulation data and the like are stored to prepare for the next data analysis;
(22) establishing an RV reducer thermal model: and establishing different gear temperature fields of the RV reducer according to different gear load spectrums obtained by ADAMS simulation. Analyzing the influence of geometric parameters, loads, lubricating characteristics and other parameters of each gear on the temperature field of each gear;
(23) establishing an RV reducer stress analysis model: according to load spectrums of different components obtained by the ADAMS simulation of the stress sensor and obtained data, the stress and the stress distribution of each point in the object such as the mechanical part and the member are analyzed and solved, and the stress concentration of dangerous points related to the failure of the mechanical part and the member and the peak stress and the strain of the strain concentration part are determined.
(24) Establishing an RV reducer fatigue model: the fatigue model is established by combining a digital twin model and actual RV reducer operation data, the actual fatigue state of the RV reducer is accurately reflected, and the future fatigue state is estimated.
(25) Data among the models supplement each other, meanwhile, historical data are compared with real-time data, when the data are different, errors are marked, and the data are displayed on an operation interface for processing;
and (3) predicting the service life of the RV reducer comprises the following processes:
(31) inputting simulation results of each model in the digital twin model into MSC.
(32) Simulating the fatigue life of the RV reducer, outputting damage data of each part, and predicting the fatigue life of each part of the RV reducer;
(33) comparing the failure standards of the parts of the RV reducer, and once one part fails, the RV reducer fails to reach the maximum service life;
(34) inputting the result into a damage evolution model;
(35) gradually constructing an RV reducer life prediction function by adopting a Kriging agent model, and establishing a high-precision RV reducer life prediction agent model by continuously collecting input data and output data;
compared with a simulation model, the proxy model has many advantages, firstly, the proxy model can replace a more complex and time-consuming numerical analysis model, secondly, the proxy model can also ensure that sample points are accurately converged to a real solution on the basis of historical data, and in the face of complex multi-dimensional problems, the proxy model ensures high approximation accuracy in important areas, particularly real solution areas, and efficiently obtains a target solution.
(36) When the reliability of the RV reducer service life prediction agent model meets the requirement, the agent model can be directly used for predicting the service life of the RV reducer, so that the simulation prediction time is saved;
the step (4) of dynamic real-time monitoring and feedback comprises the following processes:
(41) recording real-time data;
(42) recording simulation data;
(43) recording of prediction data;
(44) comparing the simulation calculation data with the real-time data;
(45) comparing the simulation prediction data with the real-time data;
(46) the service life of the RV speed reducer is analyzed according to different service lives of the RV speed reducer under different working conditions and environments, the particle swarm optimization is adopted to optimize various parameters of the RV speed reducer so as to prolong the service life, an optimal parameter suggestion of the RV speed reducer can be given under a specific environment, and data reference is provided for parameter design of the RV speed reducer.
The step (46) of parameter optimization design comprises the following processes:
(461) determining an influence parameter and a value range thereof;
(462) substituting the particle swarm algorithm, and calculating a service life result by applying a kriging model;
initializing a population
The dimension (array element number) of the particle swarm is 11, which is respectively the working load, the radius of the pin gear, the radius of the central circle of the pin gear, the eccentric distance of the crankshaft, the modification quantity of the displacement distance of the cycloid gear, the modification quantity of the equal distance of the cycloid gear, the clearance between the inner hole of the cycloid gear and the shaft sleeve of the rotating arm, the clearance between the crankshaft and the supporting shaft sleeve, the clearance between the upper supporting shaft sleeve and the planet carrier and the clearance between;
particle population size N: taking a general value of 200;
for each particle, there are two attributes:
position attribute:
Xi(t)＝{xi,1(t),xi,2(t),Λ,xi,j(t)}； (2)
the speed attribute is as follows:
Vi(t)＝{vi,1(t),vi,2(t),Λ,vi,j(t)}； (3)
where t denotes the t-th iteration (the t-th session), i denotes the number of this particle as i, j denotes the dimension of the search space, and j is 2 for the particle (search in plane);
computing individual optimal value and global optimal value
The ith particle searched to the optimal position so far is called the individual extremum:
Pbest＝(pi1,pi2,Λ,pij),i＝1,2,ΛN (4)
the optimal position searched by the whole particle swarm so far is a global extremum:
gbest＝(g1,g2,Λ,gj) (5)
after finding the two optimal values, the particle swarm updates the speed and the position of the particle swarm
Velocity update formula:
wherein, c1Is a self-learning factor, c2Is a global learning factor, all of which are constant factors, and c is taken1＝c2＝2.5；r1,r2Is [0,1 ]]The uniform random number in the range increases the randomness of particle flight;
is the particle velocity, found by study, set vmaxAnd the effect of adjusting the inertial weight is equivalent, so vmaxGenerally used for initialization setting, and taking a value of 10; λ is a compression factor for controlling the final convergence and increasing the convergence rate, wherein
Location update formula:
xij(t+1)＝xij+vij(t+1) (8)
and fourthly, determining whether the convergence standard of the algorithm is met. If so, ending the operation; if not, go to step two.
The method for predicting the service life of the RV reducer based on the digital twinning has the following beneficial effects:
1. the method solves the problems of low prediction precision, high cost and long duration of the service life of the conventional RV reducer, and provides a new method for further improving the service life prediction of the RV reducer.
2. The measurement error generated in the artificial measurement experiment process is avoided through modeling simulation, and the real-time data is adopted for processing and analyzing, so that the prediction result is more accurate.
3. According to the method, the virtual simulation technology and the agent model are combined to research the use of the RV reducer, so that the service life of the RV reducer is quickly and accurately predicted, a physical prototype test is not required, the research cost and time are saved, and the research efficiency is greatly improved.
Drawings
FIG. 1 is a model diagram of parameters of various parts of the RV reducer of the invention; fig. 1-a shows a planet carrier, fig. 1-b shows a gland, fig. 1-c shows a pin gear housing, fig. 1-d shows a planet gear, fig. 1-e shows a sun gear, fig. 1-f shows a pin gear, fig. 1-g shows a crankshaft, fig. 1-h shows a cycloid gear, fig. 1-i shows a cylindrical roller bearing (upper bearing sleeve), fig. 1-j shows a tapered roller bearing (lower bearing sleeve), and fig. 1-k shows a main bearing;
FIG. 2 is a three-dimensional model diagram of the RV reducer of the present invention;
FIG. 3 is an exploded view of the RV retarder of the present invention;
FIG. 4 is a chart of the RV retarder life prediction steps of the present invention;
FIG. 5 is a flow chart of the present invention for building a dynamic simulation model;
FIG. 6 is a schematic diagram of the dynamics simulation process of the present invention;
FIG. 7 is the results of the kinetic simulation of the present invention;
FIG. 8 is a flow chart of the present invention for building a proxy model for life prediction;
FIG. 9 is a flow chart of particle swarm optimization of the present invention;
FIG. 10 is a technical roadmap for predicting the service life of RV reducers of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention aims to design a service life prediction method of an RV reducer based on digital twinning, overcomes the defects of the conventional service life prediction method of the RV reducer, and combines the digital twinning technology to perform more accurate service life prediction on the RV reducer.
FIG. 5 is a flow chart showing the establishment of a simulation model, comprising the steps of:
(221) drawing a 3D model of the RV reducer by using SolidWorks software 1:1 to form a geometric model;
(222) importing key parts into ANYSS, selecting units, defining materials, dividing networks, and defining external nodes and rigid areas;
(223) and importing the simulation model into ADAMS software, replacing part of flexible bodies of key parts, defining the cycloidal gear, the pin gear, the crank shaft and the planet carrier into the flexible bodies, and setting the rest parts as rigid bodies to form a dynamic model.
FIG. 8 is a flow chart of kriging agent model life prediction. Firstly, after multiple times of dynamics analysis and service life analysis are carried out in a digital twin model to obtain a response value, sensor data and the response value are substituted into a kriging proxy model for curve fitting, and an initial proxy model is established; and then, the precision of the proxy model is improved in the optimization of the historical data and the proxy model, on one hand, the acquisition of the real-time data of the sensor can input more data into the proxy model, and the model is continuously corrected. And on the other hand, parameter combinations around the fitting curve are sampled through hypercube sampling, and the parameter combinations are substituted into a dynamic model in the digital twin model for analog simulation to obtain response values, so that the precision of the proxy model is improved step by step. And finally, the high-precision proxy model can quickly predict the service life of the RV reducer, and a solution is provided for realizing an algorithm in the feedback of the next step.
FIG. 9 is a RV reducer service life prediction technique roadmap, the whole prediction process being a closed loop. The method comprises the steps of generating a corresponding dynamic model, a thermal model and a stress model according to real-time data and working conditions of a physical model, filtering data received by a sensor and transmitting the data to a multi-body dynamic model, receiving working condition data actually measured by the physical model by the multi-body dynamic model, simulating by combining the filtered real-time part size to obtain predicted simulation data, entering a prediction stage, predicting the service life of the RV reducer by combining service life analysis software and a kriging proxy model, comparing the predicted value with the actually measured value, and updating a digital twin model. And finally, guiding the operation of the physical model and optimally designing the RV reducer. In fig. 9, the sensor data includes: the device comprises a working load, a pin tooth radius, a pin tooth center circle radius, a crank shaft eccentricity, a cycloidal gear displacement modification amount, a cycloidal gear equidistant modification amount, a cycloidal gear inner hole and rotating arm shaft sleeve gap, a crank shaft and supporting shaft sleeve gap, an upper supporting shaft sleeve and planet carrier gap, a lower supporting shaft sleeve and gland gap and the like.
Claims (5)
1. The service life prediction method of the RV reducer based on the digital twin comprises the following steps:
(1) establishing a data acquisition system: the method comprises the following steps that data collection is carried out on working conditions and environmental parameters of the RV reducer during actual working by using a sensor, wherein the data collection comprises a torque sensor, a vibration sensor and a temperature sensor, and the collected content comprises data such as geometric shapes of parts of the RV reducer, material properties, operating environmental parameters, torque, return difference and disturbance;
(2) establishing a digital twin model of the RV reducer: establishing a digital twin model of the RV reducer by establishing each sub-model of the digital twin model, and ensuring that real-time data is comprehensively analyzed; the submodels of the digital twin model comprise a geometric model, a dynamic model, a thermal model and a stress distribution model; acquiring real-time data through a data acquisition system, and uploading the data to the twin model; processing the uploaded data, and performing analog simulation in each sub-model;
(3) predicting the service life of the RV reducer: the sub-model can obtain the predicted service life of the RV reducer through simulation; by recording the prediction conditions of the input parameters and the output life values of the RV reducer, after enough times of operation, a kriging proxy model can be used for replacing a simulation model to predict the life; according to the data prediction of the sub-model operation and the actual operation, the data are updated to each sub-model of the digital twin model in time through the updating iteration of the data, and the accurate service life value of the RV reducer is obtained;
(4) dynamic real-time monitoring and feedback: the actual running condition of the RV reducer is monitored through real-time data acquisition, so that the reliability of twin model prediction can be ensured; the actual running condition of the RV reducer can be synchronized with the twin model and the physical model in a data mode, and the simulation data and the actual data are integrated through one-time simulation, so that the running condition of the RV reducer is accurately monitored and predicted; by applying the particle swarm algorithm, the optimized RV reducer parameter combination can be obtained, the service life of the RV reducer can be prolonged, and a data basis is provided for the design of the RV reducer.
2. The digital twin based RV reducer service life prediction method of claim 1 characterized by: the step (1) of establishing the data acquisition system comprises the following processes:
(11) a sensor is additionally arranged in the RV reducer and used for recording dynamic data of the RV reducer in real time when the RV reducer is used; the sensors include the following categories and uses:
(12) in the actual use of the RV reducer, signals generated by each sensor are converted into a spectrogram by extracting useful information through a wavelet transformation technology;
compared with Fourier transform, the time of each component can be obtained by using wavelet transform to receive signals, the condition of the signal frequency changing along with the time, the instantaneous frequency and the amplitude thereof at each moment are known, and the instantaneous frequency is analyzed; when the signal is suddenly changed, the wavelet transformation can accurately express the change and reflect the change to a time spectrum, so that the failure condition of each part of the RV reducer can be conveniently found;
(13) data uploading and classified storage.
3. The digital twin based RV reducer service life prediction method of claim 1 characterized by: the step (2) of establishing the RV reducer digital twin model comprises the following processes:
the digital twin model comprises a plurality of submodels, namely a geometric model, a dynamic model, a thermal model, a stress analysis model and a fatigue model which have the same size with the physical model part;
(21) establishing a geometric model and a dynamic model of the RV reducer: modeling 1:1 is carried out on the RV reducer by using modeling software, so that the RV reducer can form mapping with an RV reducer entity, and simulation calculation is carried out better;
the step (22) of establishing the geometric model and the dynamic model comprises the following processes:
(211) collecting and uploading real-time data;
(212) drawing a 3D model of the RV reducer according to the real-time data 1:1 by using SolidWorks software to form a geometric model;
wherein, the parameter equation of the tooth profile of the cycloidal gear is as follows:
K1-short amplitude factor;
e-eccentricity of the cycloid wheel;
iH-cycloidal-pin gear transmission ratio;
rp-the centre circle radius of the pin teeth;
rrp-the needle tooth radius;
the data are actually measured by the sensor;
(213) guiding key parts of the RV reducer in the geometric model into ANYSS, selecting units, defining materials, dividing a network, and defining external nodes and rigid areas;
(214) importing the geometric model into ADAMS software, replacing part of flexible bodies of key parts, defining cycloidal gears, pin teeth, crankshafts and planet carriers into the flexible bodies, setting the rest of the flexible bodies as rigid bodies, and adding constraints to form a dynamic model;
and (3) modeling the SolidWorks and storing the SolidWorks into an intermediate format file: x _ t, the parasolid format, is then introduced into ADAMS;
the material properties of each component are determined by the actual RV reducer material, taking the model of RV-40E as an example, the material property diagram of each component is shown in the following table:
the constraints for adding the components are shown in the following table:
(215) performing dynamic simulation according to data such as input torque, return difference and the like of the RV reducer measured in real time;
(216) outputting a load spectrum of an output shaft of the RV reducer; necessary data such as real-time data, simulation data and the like are stored to prepare for the next data analysis;
(22) establishing an RV reducer thermal model: establishing different gear temperature fields of the RV reducer according to different gear load spectrums obtained by ADAMS simulation; analyzing the influence of geometric parameters, loads, lubricating characteristics and other parameters of each gear on the temperature field of each gear;
(23) establishing an RV reducer stress analysis model: according to different component load spectrums and obtained data obtained by the ADAMS simulation of the stress sensor, analyzing and solving the stress and stress distribution of each point in objects such as mechanical parts and components, and determining the stress concentration of dangerous points related to the failure of the mechanical parts and components and the peak stress and strain of the strain concentration part;
(24) establishing an RV reducer fatigue model: the fatigue model is established by combining a digital twin model and actual RV reducer operation data, the actual fatigue state of the RV reducer is accurately reflected, and the future fatigue state is estimated;
(25) and data among the models supplement each other, historical data and real-time data are compared, and when data are different, errors are marked and displayed on an operation interface for processing.
4. The digital twin based RV reducer service life prediction method of claim 1 characterized by: and (3) predicting the service life of the RV reducer comprises the following processes:
(31) inputting simulation results of each model in the digital twin model into MSC.
(32) Simulating the fatigue life of the RV reducer, outputting damage data of each part, and predicting the fatigue life of each part of the RV reducer;
(33) comparing the failure standards of the parts of the RV reducer, and once one part fails, the RV reducer fails to reach the maximum service life;
(34) inputting the result into a damage evolution model;
(35) gradually constructing an RV reducer life prediction function by adopting a Kriging agent model, and establishing a high-precision RV reducer life prediction agent model by continuously collecting input data and output data;
compared with a simulation model, the agent model has a plurality of advantages, firstly, the agent model can replace a more complex and time-consuming numerical analysis model, secondly, the agent model can also ensure that sample points are accurately converged to a real solution on the basis of historical data, and in the face of complex multi-dimensional problems, the agent model ensures high approximation precision in important areas, particularly real solution areas, and efficiently obtains a target solution;
(36) when the reliability of the RV reducer service life prediction agent model meets the requirement, the agent model can be directly used for predicting the service life of the RV reducer, and the simulation prediction time is saved.
5. The digital twin based RV reducer service life prediction method of claim 1 characterized by: the step (4) of dynamic real-time monitoring and feedback comprises the following processes:
(41) recording real-time data;
(42) recording simulation data;
(43) recording of prediction data;
(44) comparing the simulation calculation data with the real-time data;
(45) comparing the simulation prediction data with the real-time data;
(46) analyzing according to different service lives of the RV reducers with different parameters under different working conditions and environments, optimizing various parameters of the RV reducers by adopting a particle swarm algorithm to prolong the service lives of the RV reducers, ensuring that an optimal parameter suggestion of the RV reducers can be given under a specific environment, and providing data reference for parameter design of the RV reducers;
the step (46) of parameter optimization design comprises the following processes:
(461) determining an influence parameter and a value range thereof;
(462) substituting the particle swarm algorithm, and calculating a service life result by applying a kriging model;
initializing a population
The dimension (array element number) of the particle swarm is 11, which is respectively the working load, the radius of the pin gear, the radius of the central circle of the pin gear, the eccentric distance of the crankshaft, the modification quantity of the displacement distance of the cycloid gear, the modification quantity of the equal distance of the cycloid gear, the clearance between the inner hole of the cycloid gear and the shaft sleeve of the rotating arm, the clearance between the crankshaft and the supporting shaft sleeve, the clearance between the upper supporting shaft sleeve and the planet carrier and the clearance between;
particle population size N: taking a general value of 200;
for each particle, there are two attributes:
position attribute:
Xi(t)＝{xi,1(t),xi,2(t),…,xi,j(t)}； (2)
the speed attribute is as follows:
Vi(t)＝{vi,1(t),vi,2(t),…,vi,j(t)}； (3)
where t denotes the t-th iteration (the t-th session), i denotes the number of this particle as i, j denotes the dimension of the search space, and j is 2 for the particle (search in plane);
computing individual optimal value and global optimal value
The ith particle searched to the optimal position so far is called the individual extremum:
Pbest＝(pi1,pi2,…,pij),i＝1,2,…N (4)
the optimal position searched by the whole particle swarm so far is a global extremum:
gbest＝(g1,g2,…,gj) (5)
after finding the two optimal values, the particle swarm updates the speed and the position of the particle swarm
Velocity update formula:
vij(t+1)＝λ·vij(t)+c1r1[pij(t)-xij(t)]+c2r2[pgj(t)-xij(t)] (6)
wherein, c1Is a self-learning factor, c2Is a global learning factor, all of which are constant factors, and c is taken1＝c2＝2.5；r1,r2Is [0,1 ]]The uniform random number in the range increases the randomness of particle flight;
is the particle velocity, found by study, set vmaxAnd the effect of adjusting the inertial weight is equivalent, so vmaxGenerally used for initialization setting, and taking a value of 10; λ is a compression factor for controlling the final convergence and increasing the convergence rate, wherein
Location update formula:
xij(t+1)＝xij+vij(t+1) (8)
determining whether the convergence standard of the algorithm is met; if so, ending the operation; if not, go to step two.
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