Embedded intelligent bearing double-measurement-point optimal layout method

Abstract

The application discloses an embedded intelligent bearing double-measuring-point optimization layout method. The method comprises the following steps: three-dimensional modeling is performed on a bearing provided with an alternative sensing measuring point; a statics model and a thermodynamics model are established according to the three-dimensional model of the bearing; a constant load is applied on the statics model to extract a structural strength parameter; a thermal force coefficient is inputted into the thermodynamics model to extract a thermal force evaluation parameter; a continuously variable load is applied on the statics model to extract a measuring point evaluation parameter of the alternative sensing measuring point; finally, a comprehensive evaluation parameter of each alternative sensing measuring point is obtained to select an optimal sensing measuring point, and the measuring point optimization layout of the embedded intelligent bearing is performed. The application comprehensively considers the influence of the measuring point position on the bearing bearing capacity and the sensor detection effect, and solves the problem that there is no basis for selecting the measuring point position of the embedded intelligent bearing.

Description

TECHNICAL FIELD

[0001] The application relates to a bearing measurement point layout method, relates to the field of bearing structure design, and particularly relates to a double-measurement-point optimization layout method for an embedded intelligent bearing. BACKGROUND

[0002] Bearing is a key component in rotating machinery, and its running state directly affects the precision, efficiency, service life and stability of the entire mechanical system. According to statistics, at least 30% of the failure of rotating machinery systems is caused by bearing failure. Online condition monitoring of bearings is crucial for mechanical equipment. Embedded intelligent bearings integrate sensors and microprocessors inside the bearing, making the sensors closer to the source of the measured signal, thereby obtaining more accurate and higher signal-to-noise ratio monitoring signals, ultimately improving the accuracy of real-time service condition monitoring of bearings and timely detecting early bearing failures, which has important application value for reducing economic losses and avoiding major accidents.

[0003] The layout of the measurement points of the embedded intelligent bearing is a key link to realize the health monitoring and fault diagnosis of the bearing, and its importance is reflected in the following aspects: accurately monitoring the bearing state, the layout of the measurement points directly affects the accuracy and comprehensiveness of the data collected by the sensors; improving the accuracy of fault diagnosis, through reasonable layout of the measurement points, the abnormal signals of the bearing can be more comprehensively captured, thereby improving the accuracy of fault diagnosis; optimizing the use of sensor resources, reasonable layout of the measurement points can optimize the use of sensors and avoid redundancy and waste; improving the reliability and safety of the system, reasonable layout of the measurement points can ensure that the monitoring system can stably operate under various working conditions and improve the reliability and safety of the system. Therefore, the research on the optimization layout of the measurement points of the embedded intelligent bearing is very important, however, the optimization layout technology of the measurement points of the embedded intelligent bearing is still in the exploratory stage, and currently mainly faces the following three challenges:

[0004] 1. Reducing the impact of measurement point slotting on bearing carrying capacity: The carrying capacity of the bearing is one of its core performance indicators, which directly affects the running efficiency and safety of the equipment. Measurement point slotting is a physical modification made to embed sensors, which changes the structural integrity of the bearing. Slotting may cause stress concentration, material strength reduction and fatigue life shortening, thereby affecting the carrying capacity of the bearing. Reducing the impact of slotting on the carrying capacity of the bearing needs to consider multiple factors, including the location, size, shape of the slotting and the type and layout of the sensors. This involves complex optimization design, which needs to use advanced tools such as finite element analysis to simulate and evaluate the impact of different design schemes on the performance of the bearing. How to reduce the impact of measurement point slotting on the carrying capacity of the bearing has become one of the challenges faced by the optimization layout of the measurement points of the embedded intelligent bearing.

[0005] 2. Improve the detection effect of the sensor: in the embedded intelligent bearing, the sensors at different measuring point positions have different monitoring sensitivities to the service state of the bearing. The signals collected by the sensors are easily affected by various interferences, which can reduce the signal-to-noise ratio of the signals and affect the detection effect. The anti-interference ability of the sensors at different measuring point positions is also different. In order to improve the detection effect, it is usually necessary to integrate multiple sensors to realize multi-sensor fusion. How to comprehensively consider these influencing factors has become one of the challenges faced by the optimization layout of the measuring points of the embedded intelligent bearing.

[0006] 3. Trade-off between bearing carrying capacity and sensor detection effect: the bearing carrying capacity and the sensor detection effect are both important evaluation parameters of the embedded intelligent bearing, but in the research on the optimization layout of the measuring points, it is difficult to take into account these two evaluation parameters. Generally, the sensor detection effect of the measuring point with little influence on the bearing carrying capacity is poor, and the sensor detection effect of the measuring point with great influence on the normal carrying capacity is excellent. How to trade off these two evaluation parameters has become one of the challenges faced by the optimization layout of the measuring points of the embedded intelligent bearing. SUMMARY

[0007] In order to solve the problems in the background art, the embedded intelligent bearing double-measuring-point optimization layout method provided by the present application is provided.

[0008] The technical scheme adopted by the present application is:

[0009] The embedded intelligent bearing double-measuring-point optimization layout method of the present application comprises:

[0010] S1: obtaining a three-dimensional model of a bearing by modeling the bearing with several alternative sensor measuring points, and establishing a statics model and a thermodynamics model of the bearing according to the three-dimensional model of the bearing.

[0011] S2: applying a constant load on the statics model of the bearing to extract a structural strength parameter of the bearing.

[0012] S3: inputting a thermal force coefficient into the thermodynamics model of the bearing to extract a thermal force evaluation parameter of the bearing.

[0013] S4: applying a continuously variable load on the statics model of the bearing to extract a measuring point evaluation parameter of each alternative sensor measuring point of the bearing.

[0014] S5: obtaining a comprehensive evaluation parameter of each alternative sensor measuring point according to the structural strength parameter, the thermal force evaluation parameter and the measuring point evaluation parameter, selecting two alternative sensor measuring points with higher comprehensive evaluation parameters as optimal sensor measuring points, opening a sensor embedding groove at the optimal sensor measuring points of the bearing and installing a sensor to obtain an embedded intelligent bearing, and completing the double-measuring-point optimization layout of the embedded intelligent bearing.

[0015] The step S1, each alternative sensor measurement point interval is arranged on the outer circumferential surface of the bearing, and a sensor embedding groove is arranged at each alternative sensor measurement point in the three-dimensional model of the bearing.

[0016] The step S1, according to the three-dimensional model of the bearing, a static model and a thermal model of the bearing are established by using a finite element analysis method.

[0017] In the establishment of the static model, the bearing is three-dimensionally modeled according to the bearing model and the slot size, and is imported into a static structure analysis module of a finite element analysis software, the material parameters of the bearing are defined, the grid, the contact and the constraint are set according to the actual situation, the scanning grid division method is adopted for the contact area and is encrypted, so as to construct the static model.

[0018] In the establishment of the thermal model, the bearing is three-dimensionally modeled according to the bearing model and the slot size, and is imported into a steady-state thermal analysis module of a finite element analysis software, the material parameters of the bearing are defined, the grid, the contact and the constraint are set according to the actual situation, the scanning grid division method is adopted for the contact area and is encrypted, so as to construct the thermal model.

[0019] The step S2, a constant load is applied to the static model of the bearing, so as to obtain a deformation nephogram, a stress nephogram and a life nephogram, and the maximum deformation value, the maximum stress value and the minimum life value of the outer ring of the bearing are extracted as the structural strength parameters, so as to evaluate the influence of the sensor measurement point position on the structural strength of the bearing.

[0020] The step S3, first, a theoretical model of the bearing is established by using the Palmgren theory, the preset load, the rotating speed and the environmental temperature are input into the theoretical model of the bearing, so as to obtain the heat generation coefficient and the heat dissipation coefficient of the bearing as the thermal coefficients, the heat generation coefficient and the heat dissipation coefficient are input into the thermal model as the heat flow coefficient and the convection coefficient, so as to obtain a temperature nephogram, and the average temperature of the outer ring of the bearing is extracted as the thermal evaluation parameter, so as to explore the influence of the sensor measurement point position on the operating temperature of the bearing.

[0021] The step S4, a continuous variable load is applied to the static model of the bearing, so as to obtain the strain of each alternative sensor measurement point under different loads, according to the established static model, the bearing load size is input, and the strain size of each alternative sensor measurement point is output, and the finite element simulation is carried out for multiple times; so as to establish a relationship database of the bearing load and the strain of the alternative sensor measurement point, according to the relationship data of the bearing load and the strain of each alternative sensor measurement point in the relationship database, a linear regression function is used for fitting, so as to obtain the bearing load-sensor measurement point strain function of each alternative sensor measurement point, and the slope and the determination coefficient in the bearing load-sensor measurement point strain function are extracted as the sensor measurement point evaluation parameters of each alternative sensor measurement point, so as to explore the influence of the sensor measurement point position on the sensor detection effect.

[0022] In step S5, for each alternative sensing point, first, the structural strength parameter, the thermal evaluation parameter and the measuring point evaluation parameter of the alternative sensing are normalized, then the normalized structural strength parameter, the thermal evaluation parameter and the measuring point evaluation parameter are reduced in dimension using a factor analysis method to obtain a comprehensive score of the alternative sensing point as a comprehensive evaluation parameter, so as to explore the influence of the measuring point position on the bearing load capacity and the sensor detection effect by using finite element analysis.

[0023] The embedded intelligent bearing double-measuring point optimization layout system of the application comprises:

[0024] The model establishing module is used to establish a bearing three-dimensional model, so as to further establish a statics model and a thermodynamics model.

[0025] The bearing structural strength module is used to extract the structural strength parameter of the bearing according to the statics model.

[0026] The bearing operating temperature module is used to extract the thermal evaluation parameter of the bearing according to the thermodynamics model.

[0027] The sensor detection effect module is used to extract the measuring point evaluation parameter according to the statics model.

[0028] The comprehensive evaluation module is used to obtain the comprehensive evaluation parameter of each alternative sensing point according to the structural strength parameter, the thermal evaluation parameter and the measuring point evaluation parameter, so as to comprehensively evaluate the alternative sensing points, obtain the optimal measuring point position and realize the optimization layout of the measuring points.

[0029] The electronic device of the application comprises a memory and a processor which are coupled with each other, wherein the memory stores program data, and the processor calls the program data to execute the method as described above.

[0030] The computer readable storage medium of the application stores program data, and the program data is executed by a processor to realize the method as described above.

[0031] The application models the bearing by statics, studies the influence of the measuring point position on the bearing structural strength by using the finite element analysis method, takes the maximum deformation value, the maximum stress value and the minimum life value of the slotted outer ring as the evaluation parameter, models the bearing by thermodynamics, studies the influence of the measuring point position on the bearing operating temperature by using the finite element analysis method, takes the maximum temperature value of the slotted outer ring as the evaluation parameter, studies the influence of the measuring point position on the sensor detection effect by using the finite element analysis, takes the sensor detection sensitivity and the detection linearity as the evaluation parameter, finally, reduces the data by using the factor analysis, calculates the comprehensive evaluation parameter of all the measuring points and selects the best measuring point position.

[0032] The application has the following beneficial effects:

[0033] 1. In this invention, the influence of measuring point layout on bearing load capacity is considered through theoretical modeling and finite element method, thereby reducing the impact of slotting on bearing load capacity.

[0034] 2. In this invention, the influence of the measurement point layout on the sensor detection effect is considered through theoretical modeling and finite element method, and the impact of slotting on the bearing load capacity is reduced.

[0035] 3. This invention solves the problem that existing embedded intelligent bearing measuring point optimization layout methods cannot take into account both bearing load capacity and sensor detection effect, and solves the problem of lack of basis for selecting the measuring point position of embedded intelligent bearings. Attached Figure Description

[0036] Figure 1 This is a flowchart of the embedded intelligent bearing dual-measuring point optimization layout method in an embodiment of the present invention;

[0037] Figure 2 This is a comprehensive evaluation diagram of the measuring points of the 6306 bearing under a 300N operating condition in an embodiment of the present invention;

[0038] Figure 3 This is a comprehensive evaluation diagram of the measuring points of the 6306 bearing under a 5000N operating condition in an embodiment of the present invention;

[0039] Figure 4 This is a comprehensive evaluation diagram of the measuring points of the 6306 bearing under a working condition of 29600N in an embodiment of the present invention;

[0040] Figure 5 This is a comprehensive evaluation diagram of the measuring points of bearing 6306 in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] like Figure 1 As shown, in a specific implementation of this invention, a typical deep groove ball bearing 6306 is selected as the research object. The specific implementation method of the embedded intelligent bearing dual measuring point optimization layout is as follows:

[0043] S1: Three-dimensional modeling of embedded intelligent bearings. To ensure the simulation accuracy of the model, necessary components such as shaft and bearing seat are introduced into the bearing model when establishing the three-dimensional model of the bearing. The slot size is determined according to the bearing size and sensor size. Generally, the radial slotting method is adopted on the outer surface of the outer ring. A number of alternative measurement points are evenly arranged on the outer surface of the outer ring. The number of alternative measurement points is generally an integer multiple of the number of rollers, and at least twice the number of rollers. The strain sensor is used as the sensor. The slot size includes slot width and slot depth. Deep groove ball bearing 6306 is a common type of bearing, with an inner diameter of 30 mm, an outer diameter of 72 mm, a thickness of 19 mm, and 8 rollers. The alternative sensor measurement points are arranged on the outer surface of the bearing outer ring. The adjacent alternative sensor measurement points are 5 degrees apart, and there are 72 alternative sensor measurement points. According to the size of the selected strain gauge, the slot width of the alternative sensor measurement point is set to 10 mm, and the depth is set to 3 mm. The three-dimensional model of the bearing is obtained after three-dimensional modeling.

[0044] Then, according to the three-dimensional model of the bearing, the static model and the thermal model of the bearing are established using the finite element analysis method. In establishing the static model, the bearing is three-dimensionally modeled according to the bearing model and the slot size, and is imported into the static structure analysis module of the finite element analysis software. The material parameters of the bearing are defined, and the grid, contact and constraint are set according to the actual situation. The contact area is divided by scanning grid division method and encrypted, so as to construct the static model. In establishing the thermal model, the bearing is three-dimensionally modeled according to the bearing model and the slot size, and is imported into the steady-state thermal analysis module of the finite element analysis software. The material parameters of the bearing are defined, and the grid, contact and constraint are set according to the actual situation. The contact area is divided by scanning grid division method and encrypted, so as to construct the thermal model.

[0045] S2: Apply constant load to the static model established according to the three-dimensional model of the bearing, so as to obtain the deformation cloud picture, the stress cloud picture and the life cloud picture, and extract the maximum deformation value, the maximum stress value and the minimum life value of the bearing outer ring as the structural strength parameters, in order to evaluate the influence of the measurement point position on the strength of the bearing structure. At the same time, the theoretical model of the bearing is established according to the bearing model. The theoretical model of the bearing is established using Harris bearing contact theory. The constant load is applied to the theoretical model, so as to obtain the roller load distribution and contact stress of the bearing under the theoretical model, and then compared with the results of the static model to verify the correctness of the finite element analysis, and to verify the grid independence, as follows:

[0046] Step 2.1, theoretical modeling and calculation of the bearing using Harris bearing contact theory. According to the bearing inner ring balance equation, the bearing roller load distribution result can be calculated.

[0047] Step 2.2, three-dimensional modeling of the bearing is performed, and the finite element analysis software is imported to establish the finite element model. According to the bearing model and the size of the slot, the three-dimensional modeling of the bearing is performed. Since the cage has little influence on the simulation of the bearing, the cage is ignored during modeling. In order to ensure the accuracy of the simulation, the bearing seat and the shaft are introduced, and the static structure analysis module of the finite element analysis software is imported. The material parameters of the bearing are defined, the contact area is divided by scanning grid division method and encrypted, and the non-contact part can be divided by tetrahedral grid division method. According to the actual situation, the contact and constraint conditions are set. Thus, the statics model is established.

[0048] Step 2.3, load is applied, and the roller load distribution is calculated. Compared with the theoretical model established by Harris bearing contact theory, the correctness of the finite element analysis is verified, and the grid independence verification is performed. The rated load of 6306 bearing is 29600N, so the radial load of 300N, 5000N and 29600N is applied to the inner surface of the inner ring to simulate the light load, medium load and heavy load of the bearing. The roller load distribution results are extracted and compared with the theoretical model results to verify the correctness of the finite element model. The grid of the contact area is continuously refined until the contact stress change rate is less than a certain range.

[0049] Step 2.4, the maximum deformation, stress and minimum life of the outer ring are extracted as the structural strength parameters of the bearing structure strength.

[0050] S3: the thermal force coefficient is input into the thermodynamics model established according to the three-dimensional model of the bearing, so as to extract the thermal force evaluation parameters of the bearing. First, the Palmgren theory is used to establish the theoretical model of the bearing. The preset load, speed and environmental temperature are input into the theoretical model of the bearing, so as to obtain the heat generation coefficient and heat dissipation coefficient of the bearing as the thermal force coefficient. The heat generation coefficient and heat dissipation coefficient are input into the thermodynamics model as the heat flow coefficient and convection coefficient respectively, so as to obtain the temperature cloud picture, and the average temperature of the outer ring of the bearing is extracted as the thermal force evaluation parameter to explore the influence of the measuring point position on the running temperature of the bearing. At the same time, the average steady-state temperature obtained by the theoretical model and the average temperature obtained by the thermodynamics model are compared to verify the correctness of the finite element analysis, as follows:

[0051] Step 3.1, the Palmgren theory is used to perform theoretical modeling and calculation on the bearing. The friction torque M of the rolling bearing can be divided into the viscous friction torque M0 caused by the lubricant and the torque M1 caused by the external load. After the friction torque of the bearing is determined, the heat generation power H of the bearing can be calculated as H=0.001Mω=1.047×10 -4 Mn.

[0052] The heat dissipation of the bearing can be mainly divided into two parts, the natural heat dissipation of the stationary part and the forced convection heat dissipation of the moving part. Since the outer ring is fixed on the bearing seat, it is naturally cooled to the air, and the contact surface has a heat transfer coefficient h v1 As follows:

[0053] h v1 = 23.0 (T h -T a ) 0.25

[0054] Where T h is the bearing surface temperature, and T a is the ambient temperature.

[0055] The inner ring, the roller and the cage rotate, so the heat transfer coefficient h v2 is calculated according to the forced convection mode, and the calculation formula is as follows:

[0056]

[0057] Where k0 is the thermal conductivity of air, v is the relative speed, D is the outer diameter of the outer ring, and v0 is the air viscosity.

[0058] Step 3.2, three-dimensional modeling of the bearing, import of finite element steady-state heat module, completion of related settings. According to the bearing model and the size of the slot, the three-dimensional modeling of the bearing is carried out. Since the cage has little influence on the bearing simulation, the cage is ignored during modeling. In order to ensure the simulation accuracy, the bearing seat and the shaft are introduced, and the finite element analysis software steady-state heat analysis module is imported. Define the bearing material parameters, and set the grid, contact and constraint conditions according to the actual situation, so as to establish the thermodynamic model.

[0059] Step 3.3, set the heat generation parameter and the heat dissipation parameter, calculate the average temperature of the bearing, compare with the theoretical model result, verify the correctness of the finite element analysis. Set the bearing speed to 600 RPM, the load to 300 N, 5000 N and 29600 N respectively. According to the theoretical model, calculate the bearing heat flux density and the convection heat dissipation coefficient, complete the finite element setting. Extract the average temperature of each node of the bearing, compare with the theoretical model, and verify the correctness of the finite element model.

[0060] Step 3.4, extract the maximum temperature of the outer ring as the thermal evaluation parameter of the influence of the bearing running temperature on the measuring point position.

[0061] S4: Apply continuous variable load on the static model of the bearing, obtain the strain of each alternative sensing measurement point under different loads, according to the established static model, take the bearing load size as input, and the strain size of each alternative sensing measurement point as output, perform multiple finite element simulations; thereby establishing a relationship database of bearing load and strain of alternative sensing measurement point, for the relationship data of bearing load and strain of each alternative sensing measurement point in the relationship database, using linear regression function for fitting, thereby obtaining the bearing load-measurement point strain function of each alternative sensing measurement point, extracting the slope and determination coefficient in the bearing load-measurement point strain function as the measurement point evaluation parameter of each alternative sensing measurement point, to explore the influence of measurement point position on the detection effect of the sensor, as follows:

[0062] Step 4.1, 20 times of simulation analysis are performed for each measurement point, the input is the radial load, and the output is the strain value of the measurement point position. The input load direction always points to the measurement point, and the load size is divided into two parts, the light load part is 500N to 5000N, and every 500N sets an input point, the heavy load part is 5000N to 30000N, and every 2500N sets an input point.

[0063] Step 4.2, extract all strain outputs, perform linear regression calculation on the data points, obtain the data point fitting equation, and calculate the determination coefficient of the data as follows:

[0064]

[0065] Wherein, R 2 is the determination coefficient; SSR is the regression sum of squares, and SST is the total sum of squares; is the i-th fitting value, y i is the i-th original value, is the average value.

[0066] Step 4.3, extract the slope of the fitting function as the sensor detection sensitivity evaluation parameter, and extract the determination coefficient of the fitting function as the sensor detection linearity evaluation parameter, thereby obtaining the measurement point evaluation parameter.

[0067] S5: Obtain the comprehensive evaluation parameter of each alternative sensing measurement point according to the structure strength parameter, thermal evaluation parameter and measurement point evaluation parameter, for each alternative sensing measurement point, first normalize the structure strength parameter, thermal evaluation parameter and measurement point evaluation parameter of the alternative sensing measurement point, then use factor analysis method to reduce the dimension of the normalized structure strength parameter, thermal evaluation parameter and measurement point evaluation parameter to obtain the comprehensive score of the alternative sensing measurement point as the comprehensive evaluation parameter, to explore the influence of measurement point position on the bearing carrying capacity and sensor detection effect by using finite element analysis;

[0068] The data is reduced in dimension using factor analysis and the final comprehensive evaluation parameters of the measuring points are calculated, two alternative sensing measuring points with higher comprehensive evaluation parameters are selected as the optimal sensing measuring points, and the optimal measuring point results are shown in Figs. Figure 2 、 Figure 3 、 Figure 4 and Figure 5 Under the working conditions of 300N and 5000N, the optimal measuring points are No. 28 and No. 46 measuring points, which are at an angle of 45 degrees from the bottom of the bearing. Under the working condition of 29600N, the optimal measuring points are No. 32 and No. 42 measuring points, which are at an angle of 30 degrees from the bottom of the bearing. According to the comprehensive scores calculated under the three working conditions, the finally selected measuring points are No. 29 and No. 45 measuring points, which are at an angle of 40 degrees from the bottom of the bearing. Finally, the embedded intelligent bearing is obtained after the sensor embedding groove is opened at the optimal sensing measuring point of the bearing and the sensor is installed, and the double-measuring-point optimal layout of the embedded intelligent bearing is completed.

[0069] The double-measuring-point optimal layout system of the embedded intelligent bearing includes a model establishing module, a bearing structure strength module, a bearing operating temperature module, a sensor detection effect module and a comprehensive evaluation module. The model establishing module is used to establish a three-dimensional model of the bearing, so as to further establish a static model and a thermal model. The bearing structure strength module is used to extract the structural strength parameters of the bearing according to the static model. The bearing operating temperature module is used to extract the thermal evaluation parameters of the bearing according to the thermal model. The sensor detection effect module is used to extract the measuring point evaluation parameters according to the static model. The comprehensive evaluation module is used to obtain the comprehensive evaluation parameters of each alternative sensing measuring point according to the structural strength parameters, the thermal evaluation parameters and the measuring point evaluation parameters, to further comprehensively evaluate the alternative sensing measuring points, to obtain the optimal measuring point position, and to realize the optimal layout of the measuring points.

[0070] In the present application, the influence of the measuring point layout on the bearing carrying capacity is considered by theoretical modeling and finite element method, and the influence of the groove opening on the bearing carrying capacity is reduced. The influence of the measuring point layout on the sensor detection effect is considered by theoretical modeling and finite element method, and the influence of the groove opening on the bearing carrying capacity is reduced. The problem that the existing embedded intelligent bearing measuring point optimal layout method cannot consider both the bearing carrying capacity and the sensor detection effect is solved.

[0071] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems, or computer program products. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can be embodied in the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) having computer usable program code embodied thereon.

[0072] The present application is described in reference to the flowchart and / or block diagram of the method, apparatus (system) and computer program product according to an embodiment of the present application. It is understood that each flow and / or block in the flowchart and / or block diagram, and a combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing device, generate a means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagram block or blocks can be implemented by computer program instructions. Figure 1 The flowchart and / or block diagram block or blocks can be implemented by computer program instructions.

[0073] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufacture product including instruction means, which implement the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagram block or blocks can be implemented by computer program instructions. Figure 1 The flowchart and / or block diagram block or blocks can be implemented by computer program instructions.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagram block or blocks can be implemented by computer program instructions. Figure 1 The flowchart and / or block diagram block or blocks can be implemented by computer program instructions.

[0075] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the claims and their equivalents. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the application indicated by the following claims.

[0076] The above detailed description is intended to be illustrative and not restrictive. The scope of the application should be determined, not with reference to the above description, but should be given sole reference to the claims appended hereto.

Claims

1. An embedded smart bearing dual measurement point optimized layout method, characterized in that, The application relates to a method for optimizing the layout of a double measuring point of an embedded intelligent bearing. S1: a three-dimensional model of a bearing provided with a plurality of alternative sensing points is obtained, a static model and a thermal model of the bearing are established according to the three-dimensional model of the bearing; S2: a constant load is applied to the static model of the bearing, thereby extracting structural strength parameters of the bearing; S3: thermal coefficients are input into the thermal model of the bearing, thereby extracting thermal evaluation parameters of the bearing; S4: a continuously variable load is applied to the static model of the bearing, thereby extracting measuring point evaluation parameters of each alternative sensing point of the bearing; S5: comprehensive evaluation parameters of each alternative sensing point are obtained according to the structural strength parameters, the thermal evaluation parameters and the measuring point evaluation parameters, two alternative sensing points with higher comprehensive evaluation parameters are selected as optimal sensing points, a sensor embedding groove is opened at the optimal sensing points of the bearing and a sensor is installed, thereby obtaining an embedded intelligent bearing, and the layout optimization of the double measuring point of the embedded intelligent bearing is completed. In the step S2, a constant load is applied to the static model of the bearing, thereby obtaining a deformation nephogram, a stress nephogram and a life nephogram, and the maximum deformation of the outer ring, the maximum stress and the minimum life of the bearing are extracted as the structural strength parameters. In the step S3, a theoretical model of the bearing is established by using the Palmgren theory, preset loads, rotating speeds and environmental temperatures are input into the theoretical model of the bearing, thereby obtaining heat generation coefficients and heat dissipation coefficients as the thermal coefficients, the heat generation coefficients and the heat dissipation coefficients are input into the thermal model as heat flow coefficients and convection coefficients, thereby obtaining a temperature nephogram, and the average temperature of the outer ring of the bearing is extracted as the thermal evaluation parameter. In the step S4, a continuously variable load is applied to the static model of the bearing, thereby obtaining the strains of each alternative sensing point under different loads, a relationship database of the loads of the bearing and the strains of the alternative sensing points is established, linear regression functions are used to fit the relationship data of the loads of the bearing and the strains of each alternative sensing point in the relationship database, thereby obtaining a bearing load-measuring point strain function of each alternative sensing point, and the slope and the determination coefficient in the bearing load-measuring point strain function are extracted as the measuring point evaluation parameters of each alternative sensing point.

2. The embedded smart bearing dual site optimized layout method of claim 1, wherein: In the step S1, the alternative sensing points are arranged at intervals on the outer circumferential surface of the bearing, and sensor embedding grooves are opened at the alternative sensing points in the three-dimensional model of the bearing.

3. The embedded smart bearing dual site optimized layout method of claim 1, wherein: In the step S1, the static model and the thermal model of the bearing are established by using a finite element analysis method according to the three-dimensional model of the bearing.

4. The embedded smart bearing dual site optimized layout method of claim 1, wherein: In the step S5, for each alternative sensing point, the structural strength parameters, the thermal evaluation parameters and the measuring point evaluation parameters of the alternative sensing point are normalized, and then the normalized structural strength parameters, the thermal evaluation parameters and the measuring point evaluation parameters are reduced in dimension by using a factor analysis method, thereby obtaining a comprehensive score of the alternative sensing point as the comprehensive evaluation parameter.

5. An embedded smart bearing dual site optimized layout system suitable for use in the method of any one of claims 1-4, wherein, The application further relates to a model establishment module for establishing a three-dimensional model of a bearing, thereby further establishing a static model and a thermal model. ​ A bearing structure strength module is configured to extract a structural strength parameter of the bearing according to a statics model; A bearing operating temperature module is configured to extract a thermal evaluation parameter of the bearing according to a thermodynamics model; A sensor detection effect module is configured to extract a measuring point evaluation parameter according to the statics model; A comprehensive evaluation module is configured to obtain a comprehensive evaluation parameter of each candidate sensor measuring point according to the structural strength parameter, the thermal evaluation parameter and the measuring point evaluation parameter, to comprehensively evaluate the candidate sensor measuring points, to obtain an optimal measuring point position, and to realize optimal layout of the measuring points.

6. An electronic device, comprising: Comprise: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor invokes the program data to execute the method according to any one of claims 1-4.

7. A computer readable storage medium having stored thereon program data, wherein, The program data is executed by the processor to implement the method according to any one of claims 1-4.

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