Reliability accelerated verification method and device for seeding machine motor driving structure

Abstract

The application provides a reliability accelerated verification method and device for a seeding machine motor driving structure, and the method comprises the following steps: determining vibration influence parameters corresponding to each motor driving mechanism based on temperature information of the motor driving mechanisms under each preset working condition; the vibration influence parameters are used to represent the relationship between mechanical vibration generated during the operation of the seeding machine and temperature rise generated by the motor driving mechanism; determining an acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter; verifying the reliability life parameters of the motor driving mechanism based on each acceleration coefficient; the reliability life parameters are obtained by analyzing a fault tree model of the motor driving mechanism; and the fault tree model is constructed based on various fault information of the motor driving mechanism. The application can realize effective reliability accelerated verification of the motor driving mechanism of the precision seed metering device, and has a simple operation process and low implementation cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural technology, in particular to a reliability accelerated verification method and device for a seeding machine motor driving structure. BACKGROUND

[0002] During the operation of a seeding machine (such as a corn seeding machine) in a field, due to the fluctuation of the operation speed of the machine set and the variability of the field ground conditions, the rotation speed of the seed metering device motor driving mechanism and other operation state parameters are constantly changing, and the seed metering device motor driving mechanism is subjected to multi-axis vibration and alternating load. Once the vibration and alternating load affect the reliability of the seed metering device motor driving mechanism, not only will it cause problems such as missed planting, repeated planting, and inaccurate planting spacing, but it may also cause greater damage to the seeding machine. Therefore, it is necessary to fully analyze the reliability of the internal components of the seed metering device motor driving mechanism and effectively test and verify it during the development process of the seed metering device motor driving mechanism.

[0003] However, in the prior art, there is little research on the reliability accelerated verification of the precision seed metering device motor driving mechanism, and the related research is not mature enough, and the applicability needs to be further verified. SUMMARY

[0004] The present application provides a reliability accelerated verification method and device for a seeding machine motor driving structure to effectively verify the reliability of the precision seed metering device motor driving mechanism.

[0005] The present application provides a reliability accelerated verification method for a seeding machine motor driving mechanism, comprising:

[0006] Based on the collected temperature information of a plurality of motor driving mechanisms under each preset working condition, the vibration influence parameters corresponding to each motor driving mechanism are determined; the vibration influence parameters are used to represent the relationship between the mechanical vibration generated during the operation of the seeding machine and the temperature rise generated by the motor driving mechanism thereof;

[0007] Based on each vibration influence parameter, the acceleration coefficient corresponding to each preset working condition is determined;

[0008] Based on each acceleration coefficient, the reliability life parameters of the motor driving mechanism are verified;

[0009] The reliability life parameters are obtained by analyzing the fault tree model of the motor driving mechanism; the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0010] The reliability accelerated verification method of the motor driving mechanism of the seeding machine provided by the application, the temperature information includes a first working condition temperature and a second working condition temperature, the first working condition temperature is a steady-state temperature of the motor in a static state, and the second working condition temperature is a steady-state temperature of the motor in a working state;

[0011] The vibration influence parameter corresponding to each motor driving mechanism is determined based on the collected temperature information of the plurality of motor driving mechanisms in each preset working condition.

[0012] The working condition temperature difference of each motor driving mechanism is determined based on the first working condition temperature and the second working condition temperature of each motor driving mechanism.

[0013] The vibration influence parameter corresponding to each motor driving mechanism is determined based on the ratio of the working condition temperature difference of each motor driving mechanism to the corresponding ambient temperature.

[0014] According to the reliability accelerated verification method of the motor driving mechanism of the seeding machine provided by the application, the acceleration coefficient corresponding to each preset working condition is determined based on each vibration influence parameter, and the acceleration coefficient corresponding to each preset working condition is determined based on each vibration influence parameter.

[0015] The ambient temperature of each motor driving mechanism under the accelerated verification condition is obtained, and the corrected ambient temperature corresponding to each motor driving mechanism is determined based on each vibration influence parameter and the ambient temperature under the corresponding accelerated verification condition.

[0016] The temperature set information of each motor driving mechanism is obtained, and the acceleration coefficient corresponding to each preset working condition is determined based on the temperature set information of each motor driving mechanism and the corresponding corrected ambient temperature; the temperature set information includes the ambient temperature in the working condition, the temperature rise information of the motor in the working condition, the temperature rise information of the motor under the accelerated verification condition, and the motor winding temperature compensation empirical value.

[0017] According to the reliability accelerated verification method of the motor driving mechanism of the seeding machine provided by the application, the motor driving mechanism includes a motor and a Hall encoder assembly; before the vibration influence parameter corresponding to each motor driving mechanism is determined based on the collected temperature information of the plurality of motor driving mechanisms in each preset working condition, the method further includes:

[0018] The fault tree model of the motor driving mechanism is called; the fault tree model is constructed based on various fault information of the motor and the Hall encoder assembly;

[0019] The total failure rate of the motor and the total failure rate of the Hall encoder assembly are determined based on the fault tree model.

[0020] determine an average life of the motor driving mechanism based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly, and an importance degree of each of the motor and the Hall encoder assembly; and the reliability life parameter of the motor driving mechanism comprises the average life of the motor driving mechanism, and the importance degree of each of the motor and the Hall encoder assembly.

[0021] According to the method, the average life of the motor driving mechanism is determined based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly.

[0022] The total failure rate of the motor driving mechanism is obtained based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly.

[0023] The reliability of the motor driving mechanism is determined based on a reliability theory model.

[0024] The average life of the motor driving mechanism is determined based on the total failure rate of the motor driving mechanism and the reliability of the motor driving mechanism.

[0025] According to the method, the average life of the motor driving mechanism is determined based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly.

[0026] The end time of each of the preset working conditions under the acceleration coefficient corresponding to the preset working condition is determined.

[0027] The importance degree parameter of the motor driving mechanism is determined according to the number of failed motor driving mechanisms in the acceleration verification test.

[0028] The average life estimation of the motor driving mechanism under each of the preset working conditions is determined according to the end time of each of the preset working conditions under the acceleration coefficient corresponding to the preset working condition by using the point estimation method and the interval estimation method.

[0029] The reliability life parameter of the motor driving mechanism is verified by using the importance degree parameter of the motor driving mechanism and the average life estimation of the motor driving mechanism.

[0030] The application further provides a reliability acceleration verification device of a motor driving mechanism of a seeding machine, comprising:

[0031] The first processing module is configured to determine a vibration influence parameter corresponding to each of the motor driving mechanisms based on the collected temperature information of the plurality of motor driving mechanisms under each of the preset working conditions, wherein the vibration influence parameter is used to represent the relationship between mechanical vibration generated during the operation of the seeding machine and temperature rise generated by the motor driving mechanism.

[0032] a second processing module, configured to determine an acceleration coefficient corresponding to each of the preset working conditions based on each of the vibration influence parameters;

[0033] a first verification module, configured to verify a reliability life parameter of the motor driving mechanism based on each of the acceleration coefficients.

[0034] The reliability life parameter is obtained by analyzing a fault tree model of the motor driving mechanism, and the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0035] The application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine when executing the program.

[0036] The application further provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine when executed by a processor.

[0037] The application further provides a computer program product, which includes a computer program, and the computer program implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine when executed by a processor.

[0038] The reliability acceleration verification method and device of the motor driving mechanism of the seeding machine provided by the application can realize effective reliability acceleration verification of the motor driving mechanism of the precision seed metering device, has a simple operation process, and is low in implementation cost. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0040] Figure 1A flowchart of a reliability accelerated verification method of a seeding machine motor driving mechanism provided by the present application is shown in FIG. 1.

[0041] Figure 2 A structure diagram of a fault tree model of a motor driving mechanism provided by the present application is shown in FIG. 2.

[0042] Figure 3 A failure time diagram of a motor driving mechanism in an accelerated verification test provided by the present application is shown in FIG. 3.

[0043] Figure 4 A motor driving mechanism failure data diagram of the motor driving mechanism under each working condition in an accelerated verification test of the motor driving mechanism provided by the present application is shown in FIG. 4.

[0044] Figure 5 A diagram analysis result diagram in an accelerated verification test of a motor driving mechanism provided by the present application is shown in FIG. 5.

[0045] Figure 6 A straight line parameter data diagram of a diagram analysis of each working condition in an accelerated verification test of a motor driving mechanism provided by the present application is shown in FIG. 6.

[0046] Figure 7 A structure diagram of a reliability accelerated verification device of a seeding machine motor driving mechanism provided by the present application is shown in FIG. 7.

[0047] Figure 8 A physical structure diagram of an electronic device provided by the present application is shown in FIG. 8. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0049] The present application will be described below with reference to the drawings. Figures 1-8 A reliability accelerated verification method and device of a seeding machine motor driving structure are described.

[0050] Figure 1 A flowchart of a reliability accelerated verification method of a seeding machine motor driving mechanism provided by the present application is shown in FIG. 1. Figure 1 as shown, comprising:

[0051] In step 110, based on the collected temperature information of the plurality of motor driving mechanisms under each preset working condition, the vibration influence parameter corresponding to each motor driving mechanism is determined; the vibration influence parameter is used to represent the relationship between the mechanical vibration generated during the operation of the seeding machine and the temperature rise generated by the motor driving mechanism thereof.

[0052] In step 120, based on the vibration influence parameters, the acceleration coefficient corresponding to each preset working condition is determined.

[0053] In step 130, based on the acceleration coefficient, the reliability life parameter of the motor driving mechanism is verified.

[0054] The reliability life parameter is obtained by analyzing the fault tree model of the motor driving mechanism, and the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0055] Specifically, the preset working condition described in the embodiment of the application refers to a simulated working condition in the sowing machine operation process, which can be determined according to the working environment temperature, seed disc torque and speed, wherein the working environment temperature can be set to 45℃, 50℃ and 55℃, the seed disc torque can be set to 1.7 or 1.8, and the speed can be set to 2000 rpm, considering the temperature control range of the thermostat, the working temperature limit of the motor driving mechanism and the temperature limit of the measuring instrument.

[0056] The vibration influence parameter described in the embodiment of the application can represent the relationship between the mechanical vibration generated during the sowing machine operation and the temperature rise generated by the motor driving mechanism, and is used to correct the acceleration coefficient of the accelerated life test.

[0057] The acceleration coefficient described in the embodiment of the application is an important parameter of the accelerated life test, which is commonly used in product reliability screening, reliability identification and other occasions, and reflects the effect of a certain acceleration stress level in the accelerated life test. The quantity is generally greater than 1.

[0058] The reliability life parameter of the motor driving mechanism described in the embodiment of the application refers to the reliability life index data of the motor driving mechanism, which can specifically include the average life of the motor driving mechanism and the importance of the internal components.

[0059] In the embodiments of the present application, a reliability acceleration verification method suitable for the motor driving mechanism of the precision seed metering device is proposed. First, based on the Failure Mode and Effects Analysis (FMEA) method and the Fault Tree Analysis (FTA) method, the failure modes, causes and effects of the motor driving mechanism are obtained, and a fault tree model is established, and then the average failure before time, importance and other reliability life parameter data of the motor driving mechanism are obtained. Then, field working condition data collection test is carried out, and the rack vibration and motor driving mechanism temperature rise data under actual working conditions are measured, the influence of vibration load on the motor driving mechanism temperature rise during the field operation of the seed metering device is analyzed, and then the acceleration coefficient of the accelerated life test is corrected. Finally, the accelerated verification test is carried out to verify the accuracy of the reliability analysis results (average failure before time and importance of each component) of the motor driving mechanism.

[0060] That is, in the embodiments of the present application, before the accelerated verification test of the motor driving mechanism of the seeding machine is carried out, the reliability analysis of the motor driving mechanism is needed, and the fault tree model is constructed according to various fault information of the motor driving mechanism by the FMEA method and the FTA method, so as to quantitatively analyze the reliability life parameters of the motor driving mechanism.

[0061] Based on the content of the above embodiments, the motor driving mechanism includes a motor and a Hall encoder component; before determining the vibration influence parameters corresponding to each motor driving mechanism based on the collected temperature information of a plurality of motor driving mechanisms under each preset working condition, the method further comprises:

[0062] The fault tree model of the motor driving mechanism is called; the fault tree model is constructed based on various fault information of the motor and the Hall encoder component;

[0063] Based on the fault tree model, the total failure rate of the motor and the total failure rate of the Hall encoder component are determined;

[0064] Based on the total failure rate of the motor and the total failure rate of the Hall encoder component, the average life of the motor driving mechanism and the importance of the motor and the Hall encoder component are determined; the reliability life parameters of the motor driving mechanism include the average life of the motor driving mechanism and the importance of the motor and the Hall encoder component.

[0065] Specifically, in the embodiments of the present application, the motor driving mechanism can be divided into a motor and a Hall encoder component. When performing quantitative and qualitative reliability analysis on the motor driving mechanism, a corresponding reliability model needs to be established. The motor driving mechanism of the precision seed metering device of the seeding machine can be divided into a motor and a Hall encoder component. In order to obtain a clearer reliability model, the following simplifications are made in the establishment of the model: (a) the components of the motor driving mechanism only have two states of working and failure; (b) the failure rates of all components do not affect each other; (c) all exponential distributions are adopted.

[0066] It should be noted that the motor is composed of an insulation system, a winding, a permanent magnet, an iron core, a brush and other structural components. Inside the motor, the failure of any one of them will cause the failure of the motor. Therefore, the reliability model of the entire motor adopts a series connection mode. Other structural components have little effect on the reliability of the motor and are not involved in the establishment of the reliability block diagram. Among them, R11, R12, R13, R14 and R15 respectively represent the reliability of the insulation system, the winding, the permanent magnet, the iron core and the brush in the motor system.

[0067] The Hall encoder component is composed of a resistor, a Hall element, a circuit and other structural components. During the design, no redundancy design is adopted, and the failure of any one of them will cause the Hall encoder to fail to work normally. Therefore, the reliability model between them is a series model. It is assumed that R21, R22 and R23 respectively represent the reliability of the resistor, the Hall element and the circuit in the Hall encoder component.

[0068] Therefore, the mathematical model of each part of the motor driving mechanism and the entire mechanism can be determined as follows:

[0069] R t =R m R h (1)

[0070] R m =R 11 R 12 R 13 R 14 R 15 (2)

[0071] R h =R 21 R 22 R 23 (3)

[0072] In the formula, Rt represents the reliability of the motor driving mechanism as a whole; Rm represents the reliability of the motor system; and Rh represents the reliability of the Hall encoder component.

[0073] The failure rate of the motor driving mechanism based on the exponential life distribution is:

[0074]

[0075] In the formula, λs represents the overall failure rate of the motor driving mechanism, λ i represents the failure rate of each component. According to the reliability theory model, the relationship between reliability and failure rate is as follows:

[0076] R = e -λt (5)

[0077] Further, the mean time to failure (MTTF), which can also be referred to as the average life, is obtained, and the calculation formula of this reliability index is as follows:

[0078]

[0079] It should be noted that the FMEA method includes failure mode analysis, failure cause analysis, failure effect analysis and other steps, that is, all possible failure modes of the components are analyzed, the causes of each failure mode are found out, the effects of each possible failure mode are found out, and the effects are classified according to the severity of the effects.

[0080] Further, the failure modes, failure causes and effects of each component of the motor driving mechanism are analyzed.

[0081] The FTA method is a reliability, safety analysis and risk evaluation method. The purpose is to analyze various causes of system failure to obtain a fault tree model of the system, and to obtain the failure probability of each component and the factors leading to system failure, and finally to obtain the failure rate of the entire system.

[0082] In the embodiment of the present application, a large amount of electronic equipment usage data in the GJB / Z 299C-2006 Electronic Equipment Reliability Prediction Manual is used to calculate the failure rate of each component of the motor driving mechanism.

[0083] The motor failure rate λ j is calculated by using the following motor failure rate model:

[0084] λ j = λ b π E π Q (7)

[0085] In the formula, λb represents the basic failure rate, πE represents the environmental coefficient, and πQ represents the quality coefficient.

[0086] The failure rate λ z of the resistor is calculated by using the following resistor failure rate model and basic failure rate model:

[0087] λ z = λ b π E π Q π R (8)

[0088]

[0089] wherein π R represents the resistance coefficient; H, J, W represent the acceleration constant of resistance; α represents the adjustment factor of failure rate level; TR represents the rated temperature; SR represents the rated stress; TA represents the rated stress; S represents the electrical stress ratio of resistance; Z represents the shape parameter of resistor.

[0090] The failure rate λ of the Hall switch is calculated by using the following failure rate model of the Hall switch, in combination with the characteristics and working conditions of the Hall switch k , then:

[0091] λ k = π Q [C1π T π V + (C3+C2)π E ]π L (10)

[0092] wherein π T represents the temperature stress coefficient; π V represents the voltage stress coefficient; π L represents the maturation coefficient; C3 represents the packaging complexity coefficient; C1, C2 both represent the circuit complexity failure rate.

[0093] The failure rate λ of the electronic circuit is calculated by using the following failure rate model of the electronic circuit, in combination with the characteristics and working conditions of the electronic circuit l , then:

[0094] λ l = (λ b1 N+λ b2 )π E π Q π C (11)

[0095] wherein π C represents the structure coefficient; N represents the number of metallized holes.

[0096] Further, the research object of the present application is the motor driving mechanism, the failure of the motor driving mechanism is determined as the top event, and the specific evaluation basis of the failure is that the motor working performance is insufficient or exceeds the specified value. The top event is taken as the starting end of the fault tree analysis, and the fault tree model is established. The most direct reason for causing the top event of the entire system to appear is determined as the second level event of the system, and then all reasons for causing the second level event to appear are searched. According to the order from top to bottom, all bottom events causing the top event of the entire system to appear are finally determined.

[0097] Figure 2 The structure diagram of the fault tree model of the motor driving mechanism provided by the present application is shown in FIG. 1. Figure 2 As shown in FIG. 1, the fault tree model includes a motor fault tree (a permanent magnet fault tree, a rotor winding fault tree, etc.) and a Hall encoder assembly fault tree. Figure 2 FIG. 1(a) is a fault tree model structure of the entire motor driving mechanism, FIG. 1(b) is a fault tree model structure of the motor, FIG. 1(c) is a fault tree model structure of the permanent magnet in the motor, FIG. 1(d) is a fault tree model structure of the rotor winding in the motor, and FIG. 1(e) is a fault tree model structure of the Hall encoder assembly.

[0098] In the embodiment of the present application, the fault top event group of the motor driving mechanism is composed of:

[0099] Y=A+B (12)

[0100] Wherein, the motor fault event is composed of stator failure, rotor failure, brush failure and other failures, then:

[0101]

[0102] The fault group of the Hall encoder assembly is composed of:

[0103]

[0104] In summary, the minimal cut set of the fault event of the motor driving mechanism is represented as:

[0105] {A, B1, B2, B3, D1, D2, D3, E1, E2, … E7, F, G, … M};

[0106] Further, according to the above fault tree model of the motor driving mechanism, quantitative analysis can be performed to calculate the failure rate of each part of the system, that is, according to the preset working condition information and the above fault tree model, the total failure rate of the motor and the total failure rate of the Hall encoder assembly can be quantitatively calculated, and then the average life of the motor driving mechanism, i.e. the average failure time, and the importance of the motor and the Hall encoder assembly, etc. Reliability life parameter data.

[0107] In this embodiment, the failure rate calculation results of each sub-component of the motor drive mechanism are as follows:

[0108] (a) For motor failure analysis, combined with preset operating condition information, including the motor's operating speed from 1000 rpm to 2500 rpm, its basic failure rate λ b = 2.6 × 10⁻⁶ / h, combined with the environmental coefficient π E =10 and the quality coefficient π Q =2.4, so the total failure rate of the motor can be calculated to be 62.4×10-6 / h.

[0109] (b) For the failure analysis of the resistor, with the electrical stress ratio S = 1 and the ambient temperature at 20°C, the basic failure rate λ of the resistor can be obtained. b = 0.007 × 10⁻⁶ / h. Then, based on the resistor's characteristics and operating conditions, and referring to the electronic equipment reliability manual, determine the resistor's environmental factor π. E =6.7, quality coefficient π Q =4.0, resistance coefficient π R =1.0. The final calculated total failure rate of the resistor is 0.1876×10-6 / h.

[0110] (c) For Hall switches, the environmental coefficient π is known. E =11, quality coefficient π Q =10, circuit complexity C1 = 0.4272 × 10⁻⁶ / h, C2 = 0.0406 × 10⁻⁶ / h, maturity coefficient π L =1.0, voltage stress coefficient π V =1.0, temperature stress coefficient π T =0.68, the packaging complexity failure rate C3 = 0.0037×10-6 / h, and the final calculated total failure rate of the Hall switch is 7.78×10-6 / h.

[0111] (d) For electronic circuits, the basic failure rate λ of electronic circuits b1 =0.00017×10⁻⁶ / h, λ² =0.0011×10⁻⁶ / h, further determining the environmental coefficient π E =8.0, quality coefficient π Q =4.5, number of metallized holes N=18, structure coefficient π C =1.0, and the final calculated total failure rate of the electronic circuit is 0.14976×10-6 / h.

[0112] It should be noted that, based on a large amount of data and historical experience, the probability of mechanical parts such as shafts failing is extremely small, so their fault tree quantitative analysis will not be considered.

[0113] In summary, in the embodiment of the application, the number of motors is 1, the number of resistors is 3, the number of Hall switches is 2, and the number of electronic circuits is 1, so the total failure rate of the motor is 62.4*10-6 / h, the total failure rate of the resistor is 0.5628*10-6 / h, the total failure rate of the Hall switch is 15.56*10-6 / h, and the total failure rate of the electronic circuit is 0.14976*10-6 / h, and thus the total failure rate of each component of the motor driving mechanism can be obtained, i.e., the total failure rate λ1 of the motor is 62.4*10-6 / h, and the total failure rate λ2 of the Hall encoder component is 16.27256*10-6 / h.

[0114] Further, based on the total failure rate of the motor and the total failure rate of the Hall encoder component, the failure rate of the motor driving mechanism as a whole can be obtained, and the reliability life parameters of the motor driving mechanism, including the average life, and the importance of the motor and the Hall encoder component, can be calculated.

[0115] Based on the content of the above embodiment, as an optional embodiment, based on the total failure rate of the motor and the total failure rate of the Hall encoder component, the average life of the motor driving mechanism is determined, including:

[0116] Based on the total failure rate of the motor and the total failure rate of the Hall encoder component, the total failure rate of the motor driving mechanism is obtained, based on the reliability theory model, the reliability of the motor driving mechanism is determined, and based on the total failure rate of the motor driving mechanism and the reliability of the motor driving mechanism, the average life of the motor driving mechanism is determined.

[0117] Specifically, in the embodiment of the application, the failure rate of the motor driving mechanism as a whole is:

[0118]

[0119] The life distribution model of the motor driving mechanism in the present study is an exponential distribution, and the average life formula is:

[0120]

[0121] It can be calculated that the average life of the motor driving mechanism is 12710.91h.

[0122] The method of the embodiment of the application can effectively improve the accuracy and effectiveness of the average life calculation of the motor driving mechanism of the seeding machine by subdividing the motor driving mechanism of the seeding machine into a motor and a Hall encoder component, calculating the failure rates of each part, and calculating the average life of the motor driving mechanism by using a classical reliability theory model.

[0123] Further, according to the failure rate of the motor driving mechanism as a whole and the failure rate of each component, the importance of the motor and the Hall encoder component is calculated, and the importance of the motor is 79.31%, and the importance of the Hall encoder component is 20.69%, and the importance of the resistor, the Hall switch and the electronic circuit is 0.72%, 19.78% and 0.19% respectively.

[0124] The method of the embodiment of the application can effectively improve the accuracy and effectiveness of the calculation of the reliability life parameters of the motor driving mechanism by analyzing the reliability of the motor driving mechanism, using the FMEA method and the FTA method to obtain the failure mode, reason and influence of the motor driving mechanism, constructing a fault tree model according to various fault information of the motor driving mechanism, and quantitatively calculating the reliability life parameters of the motor driving mechanism in combination with various working condition parameters, thereby providing reliable verification source data for subsequent accelerated verification tests of the motor driving structure.

[0125] Further, the accelerated verification test of the motor driving mechanism of the seeding machine is started. In the embodiment of the application, the motor driving mechanism accelerated verification test bench is built by using the same dynamic test system to obtain the motor temperature rise data and the reliability life index data of the motor driving mechanism under each simulated working condition. The test bench is loaded with four seed plates and the motor driving mechanism. The size of the test bench is 500mm in length, 400mm in width and 700mm in height. The accelerated verification test bench has several advantages: first, the accelerated verification test bench is in the room, the environment is relatively simple, the interference factors are small, and the entire test process can be copied and the repeated test is relatively simple; second, the motor rotation speed is measured and controlled by using an automatic device, and the speed can be basically kept constant and the error is small.

[0126] In the embodiment of the application, according to the comprehensive test design method, the preset simulated working conditions are refined into 6 kinds, and each working condition is repeated for 3 times of temperature rise tests and 4 times of accelerated life tests, wherein, working condition 1: the working environment temperature is 45℃, the seed plate torque is 1.7N·m, and the rotation speed is 2000rpm; working condition 2: the working environment temperature is 45℃, the seed plate torque is 1.8N·m, and the rotation speed is 2000rpm; working condition 3: the working environment temperature is 50℃, the seed plate torque is 1.7N·m, and the rotation speed is 2000rpm; working condition 4: the working environment temperature is 50℃, the seed plate torque is 1.8N·m, and the rotation speed is 2000rpm; working condition 5: the working environment temperature is 55℃, the seed plate torque is 1.7N·m, and the rotation speed is 2000rpm; and working condition 6: the working environment temperature is 55℃, the seed plate torque is 1.8N·m, and the rotation speed is 2000rpm.

[0127] In the embodiment of the application, in order to obtain the vibration and temperature (temperature rise) data of the motor driving mechanism under each preset working condition, a prototype dynamic test system is built, vibration and temperature parameter tests are carried out under the actual field working condition and the field static condition of the seeding machine, the corn seeding machine and the motor driving mechanism of a certain manufacturer are taken as the test objects, 11 test points are selected for testing, and CA-YD-185 piezoelectric acceleration sensors and WZP gasket type temperature sensors are used.

[0128] The vibration data of the test points under different motor rotating speeds when the seeding machine is static is analyzed in terms of statistical characteristic values. The acceleration effective value is used to measure the vibration level, which can systematically reflect the vibration intensity of the seeding machine and then reflect the severity of the working condition of the seeding machine.

[0129] Through the analysis of the vibration data of different parts of the seeding machine under each working condition, the vibration intensity of the seeding machine is related to the seeding operation speed, and the greater the operation speed, the more intense the vibration of the seeding machine. The vibration intensity of the seeding machine has a strong correlation with the seeding operation speed, and the vibration intensity of the seeding machine increases with the increase of the seeding operation speed.

[0130] At the same time, through the analysis of the temperature data of each test point under different preset working conditions, the steady-state temperature of the motor under different preset working conditions gradually increases with the increase of the seeding speed.

[0131] In the embodiment of the application, the acceleration test is carried out on the motor driving mechanism, and the vibration data is a key factor. At the same time, since vibration can cause temperature rise, temperature is a main factor causing failure of the motor driving mechanism. Therefore, through the field working condition data acquisition test, the influence relationship between vibration and motor temperature rise is obtained, and the vibration factor is considered in the acceleration life test of the motor driving mechanism.

[0132] Further, in step 110, based on the collected temperature information of the plurality of motor driving mechanisms under each preset working condition, vibration influence parameters corresponding to each motor driving mechanism are obtained.

[0133] Based on the content of the above embodiment, as an optional embodiment, the foregoing temperature information includes a first working condition temperature and a second working condition temperature, the first working condition temperature is the steady-state temperature of the motor under the static condition, and the second working condition temperature is the steady-state temperature of the motor under the working condition;

[0134] Based on the collected temperature information of the plurality of motor driving mechanisms under each preset working condition, vibration influence parameters corresponding to each motor driving mechanism are determined, including:

[0135] determining a working condition temperature difference of each motor driving mechanism based on the first working condition temperature and the second working condition temperature of each motor driving mechanism;

[0136] obtaining a vibration influence parameter corresponding to each motor driving mechanism based on a ratio of the working condition temperature difference of each motor driving mechanism and the corresponding ambient temperature.

[0137] Specifically, the first working condition temperature described in the embodiments of the present application refers to a steady-state temperature of the motor after the motor driving mechanism of the seeding machine is subjected to vibration in a static working condition, and the second working condition temperature refers to a steady-state temperature of the motor after the motor driving mechanism of the seeding machine is subjected to vibration in a working condition.

[0138] In the embodiments of the present application, vibration and temperature (temperature rise) information of each measuring point on the seeding machine and the motor driving mechanism under each preset working condition is obtained through a dynamic test system. The temperature information includes temperature information under a field working condition and a field static working condition. As for the field working condition, the forward speed of the seeding machine is four seeding speeds of 4, 6, 8 and 10 km / h. As for the field static working condition, the seed disc rotating speed, i.e. the motor rotating speed, is four cases of 1000 rpm, 1500 rpm, 2000 rpm and 2500 rpm. The field working condition and the field static working condition are consistent in other factors except for the difference between whether the seeding machine is in a running state.

[0139] In the embodiments of the present application, a vibration influence parameter K is set, and its expression is as follows.

[0140]

[0141] In the formula, T0 represents the ambient temperature when the motor is working; T1 represents the steady-state temperature of the motor in the static working condition, i.e. the first working condition temperature; and T2 represents the steady-state temperature of the motor in the working condition, i.e. the second working condition temperature.

[0142] In the embodiments, according to the expression of the vibration influence parameter, the first working condition temperature T1 and the second working condition temperature T2 of each motor driving mechanism are obtained, the working condition temperature difference T2-T1 of each motor driving mechanism is calculated, and then based on the ratio of the working condition temperature difference T2-T1 of each motor driving mechanism and the corresponding ambient temperature T0, the vibration influence parameter K corresponding to each motor driving mechanism is obtained.

[0143] In a specific embodiment of the present application, for the convenience of description, three motor test cases are described. Through the temperature data of each measuring point under different preset working conditions, the change of the steady-state temperature of the seeding working condition compared with the seeding static working condition of each motor under different motor rotating speeds is indicated by the vibration influence parameter K, and the specific case is shown in Table 1. In the table, the ambient temperature T0 when the motor is working can be set to 19℃.

[0144] Table 1

[0145]

[0146] The method of the embodiment of the application considers the vibration factor in the accelerated life test of the motor driving mechanism, and is used for correcting the test results obtained in the accelerated life test, thereby improving the accuracy and reliability of the accelerated life test of the motor driving mechanism.

[0147] Further, in step 120, the acceleration coefficient corresponding to each preset working condition is calculated based on the improvement of the acceleration coefficient according to each vibration influence parameter.

[0148] Based on the above embodiment, as an optional embodiment, in step 120, the acceleration coefficient corresponding to each preset working condition is determined based on each vibration influence parameter, and the method comprises the following steps of:

[0149] The environmental temperature of each motor driving mechanism under the accelerated verification condition is obtained, and the corrected environmental temperature corresponding to each motor driving mechanism is determined based on each vibration influence parameter and the environmental temperature under the corresponding accelerated verification condition.

[0150] The temperature set information of each motor driving mechanism is obtained, and the acceleration coefficient corresponding to each preset working condition is determined based on the temperature set information of each motor driving mechanism and the corresponding corrected environmental temperature; the temperature set information comprises the environmental temperature under the working condition, the temperature rise information of the motor under the working condition, the temperature rise information of the motor under the accelerated verification condition, and the motor winding temperature compensation empirical value.

[0151] It should be noted that the key to realizing the accelerated life test is to establish the relationship between the life characteristics and the stress level, that is, the acceleration model. The Arrhenius model is the most classic and widely used acceleration model.

[0152] Specifically, the accelerated verification condition described in the embodiment of the application refers to the condition of the motor driving mechanism in the accelerated verification test process.

[0153] In this embodiment, according to the Arrhenius model and the average insulation life basic formula of the motor winding insulation thermal aging, the expression of the Arrhenius model is as follows:

[0154]

[0155] In the formula, ξ represents the life characteristics, which is selected according to the different life distribution types; A0 represents a constant related to the specific sample characteristics and the test method; E represents the failure mechanism activation energy related to the material; k represents the Boltzmann constant; and T represents the thermodynamic temperature (K).

[0156] Further, based on the Arrhenius model, the expression of the average insulation life of the winding insulation thermal aging can be derived as follows:

[0157]

[0158] In the formula, L represents the average insulation life; T represents the temperature of the insulation material; k represents the Boltzmann constant; E α G, B are all coefficients related to the insulation material,

[0159] Further, in the embodiment of the present application, considering the influence of vibration on temperature rise and the difference between the winding temperature of the motor and the surface temperature of the motor, the acceleration coefficient A is improved, and the acceleration coefficient A can be expressed as:

[0160]

[0161] In the formula, L i represents the life of the motor drive mechanism under accelerated stress conditions, L0 represents the life of the motor drive mechanism under normal stress conditions, T0 represents the ambient temperature under working conditions; T 11 represents the temperature rise information of the motor under working conditions; T0' is the ambient temperature under accelerated verification conditions; T 21 represents the temperature rise information of the motor under accelerated verification conditions; K represents the vibration influence parameter; T 22 represents the motor winding temperature compensation empirical value.

[0162] In the embodiment of the present application, based on the above-mentioned acceleration coefficient model, the ambient temperature of each motor drive mechanism under the accelerated verification conditions is obtained, and based on the vibration influence parameter and the ambient temperature under the corresponding accelerated verification conditions, the corresponding corrected ambient temperature K*T0' of each motor drive mechanism is obtained.

[0163] Further, by obtaining the temperature set information of each motor drive mechanism, the ambient temperature under working conditions, the temperature rise information of the motor under working conditions, the temperature rise information of the motor under accelerated verification conditions and other data are obtained, and according to the temperature set information of each motor drive mechanism and the corresponding corrected ambient temperature, the acceleration coefficient A corresponding to each preset working condition can be calculated.

[0164] In one specific embodiment of the present application, by temperature rise test, the steady-state temperature data of the motor drive mechanism under six preset working conditions are obtained, and the acceleration coefficient corresponding to each preset working condition is obtained by the above-mentioned acceleration coefficient calculation formula, as shown in Table 2.

[0165] Table 2

[0166]

[0167] The method of the embodiment of the present application can further improve the effectiveness and accuracy of the accelerated life test of the motor driving mechanism by considering the influence of vibration on temperature rise and the difference between the winding temperature of the motor and the surface temperature of the motor, and using the vibration influence parameter to improve and correct the acceleration coefficient in the accelerated life test.

[0168] Further, in step 130, based on the acceleration coefficient under each preset working condition, the test end time corresponding to each preset working condition is calculated, and the reliability life parameters of the motor driving mechanism can be verified by combining the point estimation method and the interval estimation method.

[0169] Based on the content of the above embodiment, as an optional embodiment, in step 130, the reliability life parameters of the motor driving mechanism are verified based on the acceleration coefficient, including:

[0170] determining the test end time corresponding to each preset working condition under the acceleration coefficient corresponding to each preset working condition;

[0171] determining the importance parameter of the motor driving mechanism according to the number of failed motor driving mechanisms in the accelerated verification test;

[0172] using the point estimation method and the interval estimation method to determine the average life estimation of the motor driving mechanism under each preset working condition according to the test end time corresponding to each preset working condition;

[0173] verifying the reliability life parameters of the motor driving mechanism by using the importance parameter of the motor driving mechanism and the average life estimation of the motor driving mechanism.

[0174] Specifically, in the embodiment of the present application, the test end time is related to the number of test samples and the expected number of failures, and the test is stopped when the cumulative failure probability in the test reaches a certain specified value. The life distribution of the motor driving mechanism adopts an exponential distribution, and its cumulative failure distribution function is:

[0175]

[0176] In the formula, θ is the average life, that is, the average time before failure, and t is the failure time random variable.

[0177] At this time, F(t)≈r / n is substituted into the above formula, and the test truncation time is obtained as:

[0178]

[0179] In the formula, θ is the average life, that is, the average time before failure, n is the total number of test samples, and r is the number of failed samples.

[0180] In the embodiment, 48 motor driving mechanisms are damaged in total through the accelerated life test of 96 motor driving mechanisms. Since the reliability is selected as 50% according to the reliability theory, it means that 48 motors should be damaged in the same accelerated verification test of 96 motors, and thus the damage is normal. In addition, in the embodiment, the reliability of 50% is set based on the fact that, according to the reliability theory, when n / 2 test pieces fail in the loading test for n test pieces, the average time before failure at this time is the average life.

[0181] Further, in the embodiment, after the acceleration coefficients corresponding to the preset working conditions are determined, the average time before failure, i.e. the average life MTTF = 12710.91 h, is obtained by means of the average time before failure obtained in the theoretical analysis part, and the truncation time of the accelerated life test of the motor driving mechanism is calculated as 8811 h according to the above formula, and then the end time of the test corresponding to each preset working condition is obtained after conversion by the acceleration coefficient, as shown in Table 3.

[0182] Table 3

[0183]

[0184] Further, according to the number of failed motor driving mechanisms in the actual verification test, the importance parameters of the motor driving mechanism, including the importance of the motor and the importance of the Hall encoder assembly, can be determined.

[0185] It should be particularly noted that the failure probability of the resistor and the electronic circuit is less than 1% in the fault tree analysis, and these two components together with the Hall switch form a sub-component of the Hall encoder, and thus in the analysis of the accelerated verification test, the failure categories of the motor driving mechanism to be compared in the theoretical analysis are sorted into two parts of the motor and the Hall encoder, and the failure probabilities are 79.31% and 20.69% respectively, i.e. the importance of the motor and the importance of the Hall encoder assembly are 79.31% and 20.69% respectively.

[0186] Further analysis of the failure mode of the motor driving mechanism shows that the specific failure category is as shown in Table 4. In the accelerated verification test, 48 motor driving mechanisms are damaged in total through the accelerated life test of 96 motor driving mechanisms, including 38 motors and 10 Hall encoders, and thus in the test result, the importance of the motor is 38 / 48 = 79.17%, and the importance of the Hall encoder assembly is 10 / 48 = 20.83%.

[0187] Therefore, in terms of the failure importance of the motor driving mechanism, the result of the accelerated verification test is basically consistent with the result of the reliability theory analysis of the foregoing embodiment of the present application, and the absolute error is 0.14%, which is within an acceptable range.

[0188] Table 4

[0189]

[0190] Further, in the embodiment, point estimation method and interval estimation method are adopted to determine the average life estimation of the motor driving mechanism under each preset working condition according to the test end time corresponding to each preset working condition.

[0191] Specifically, in the embodiment, for the accelerated life test result analysis of the research object motor driving mechanism, the main links include point estimation of test results, interval estimation of test results and test result graph analysis.

[0192] Through the point estimation of test results, the average life estimation value of the motor driving mechanism under each working condition of the accelerated verification test can be obtained; through the interval estimation of test results, the interval range of the average life of the motor driving mechanism under each working condition under a certain confidence level can be obtained; through the test result graph analysis method, whether the life distribution model of the research object motor driving mechanism and the result of the accelerated verification test are true and reliable can be verified.

[0193] The specific steps are as follows:

[0194] (1) Point estimation of test results:

[0195] For the research object motor driving mechanism, the accelerated life test form is [n, no, t0], which is characterized by n test samples, no replacement during the test process, and t0 as the censored time.

[0196] For the no-replacement fixed-time censored life test, if there are r failures in n samples at the specified test time, the total test time of n samples is:

[0197]

[0198] At this time, the point estimation value of the average life is:

[0199]

[0200] (2) Interval estimation of test results:

[0201] The average life data obtained by point estimation is based on n test samples, and there is a certain error. Therefore, to obtain the range of the average life with certain accuracy, interval estimation method and the concept of confidence interval are required.

[0202] In the embodiment, the confidence interval (θ L ,θ U ), the upper confidence limit θ U , and the lower confidence limit θ LThe relationship between the significance level or risk degree α, and the confidence (1-α) is shown in equation (23).

[0203] P{θ L <θ<θ U}=1-α (23)

[0204] The life of the motor driving mechanism is exponentially distributed, and the probability of θ in the range of (a, b) is:

[0205]

[0206] In fact, for convenience, the distribution function of θ is not directly calculated, but 2r / θ = 2t / θ (in which t is the total test time) is calculated, and then:

[0207]

[0208] In fact, this distribution is a χ2distribution with 2r degrees of freedom, so from the meaning of quantile, we have:

[0209]

[0210] Therefore, the confidence interval of θ with a confidence level of (1-α) (θ L ,θ U ) can be determined by the following equation:

[0211]

[0212] (3) Graph analysis method for accelerated verification test results:

[0213] In this embodiment, the graph analysis method is used to evaluate whether the reliability life model and test results are accurate. The life of the motor driving mechanism is exponentially distributed, and its function is a curve. After logarithmic processing, the following linear function can be obtained:

[0214] y=x (28)

[0215] In which:

[0216]

[0217] x=-ln[1-F(t)] (30)

[0218] In the equation, A represents the acceleration coefficient, θ represents the average life, t represents the failure time under the accelerated verification environment, and F(t) represents the failure probability value.

[0219] It can be seen that the exponential distribution function is a straight line with a slope of 1 and passing through the origin in the coordinate system of y = At / θ and x = -ln[1-F(t)]. Therefore, the failure data of the motor driving mechanism obtained by carrying out the accelerated verification test should be configured as a straight line with a slope of 1 and passing through the origin in the coordinate system, and if not, it indicates that the data does not conform to the exponential distribution, that is, the life distribution model is not selected correctly, and vice versa.

[0220] In the formula, i is the failure order, and n is the total number of test pieces.

[0221]

[0222] In the formula, i is the failure order, and n is the total number of test pieces.

[0223] Figure 3 The failure time diagram of the motor driving mechanism in the accelerated verification test provided by the application is shown in FIG. 1. Figure 3 As shown in the embodiment of the application, 96 motor driving mechanisms are subjected to accelerated life tests, and a total of 48 motor driving mechanisms are damaged, wherein the censored means that the motor driving mechanisms are not damaged until the test censored time. In order to ensure the accuracy of the test data, 4 tests are repeated for each working condition, that is, test 1, test 2, test 3 and test 4.

[0224] Further, according to the failure time data of the motor driving mechanism under each working condition in the figure, the point estimation method and the interval estimation method are used to calculate the average life point estimation value and the average life interval range under 90% confidence of the motor driving mechanism under test under 6 working conditions by formula (21) to formula (28), and the specific results are shown in Table 5.

[0225] It can be known from Table 5 that in the reliability theory analysis of the application, the average life of the motor driving mechanism is 12710.91h, and the average value of the average life point estimation value of the accelerated life test is 13136.20h, and the difference between the two is small, and the relative error is only 3.3%. In addition, the average value of the interval estimation range is (7280.45h, 26397.8h), and it can be known that the average life in the theoretical analysis is also located in the interval estimation with a confidence of 90% obtained from the accelerated life test data. However, there is still an error between the test result and the theoretical analysis result, because the accelerated stress (environmental temperature and load) selected in the accelerated life test, although it is the main failure factor of the motor driving mechanism, other factors also act at the same time, which will cause the failure time obtained by the accelerated life test to be slightly large, thereby causing the average life estimation value to be slightly large.

[0226] Table 5

[0227]

[0228] Furthermore, a graphical analysis was performed on the failure data of the motor drive mechanism. Using the failure time data of the motor drive mechanism under various operating conditions, At / θ and -ln[1-F(t)] were obtained using formulas (29) to (31), as shown in the following figures. Figure 4 As shown. A graphical analysis method is used to analyze... Figure 4 Effective analysis was performed on the data, and some analysis results are as follows: Figure 5 As shown.

[0229] Figure 5 This is a schematic diagram of the graphical analysis results from the accelerated verification test of the motor drive mechanism provided by this invention, as shown below. Figure 5 As shown, this is the graphical analysis result corresponding to working condition 2, where "Slope" represents the slope of the straight line and "Intercept" represents the intercept. Figure 5 Figure (a) shows the graph analysis results for Experiment 1 under scenario 2, with a slope k = 0.9737 and an intercept b = 0.00577. Figure (b) shows the graph analysis results for Experiment 2 under scenario 2, with a slope k = 0.9891 and an intercept b = 0.00471. Figure (c) shows the graph analysis results for Experiment 3 under scenario 2, with a slope k = 0.969 and an intercept b = 0.00612. Figure (d) shows the graph analysis results for Experiment 4 under scenario 2, with a slope k = 0.969 and an intercept b = 0.0072. The linear parameter data in the graph analysis of the other scenarios in the accelerated verification experiment are as follows: Figure 6 As shown, based on the linear parameter data corresponding to each working condition, the graphical analysis results for each working condition can be obtained.

[0230] In the embodiments of the present invention, based on the graphical analysis results of the above-mentioned operating conditions, it can be seen that the mean slope of the fitted straight line of the failure data for the six test conditions is 0.9671, the relative error of the slope compared to the straight line with a slope of 1 is 3.29%, and the mean intercept is 0.00951. The fitted straight line of the failure data passes well close to the origin. Therefore, the life distribution of the motor drive mechanism basically conforms to the exponential distribution, and the test data obtained from the accelerated life test is also accurate and reliable.

[0231] In embodiments of the present invention, the reliability life parameters of the motor drive mechanism are verified using the importance parameters and the average life estimate of the motor drive mechanism. The results of the importance analysis and average life analysis of the motor drive mechanism correspond well with the reliability life parameters obtained from the aforementioned reliability theory analysis. Regarding importance, the absolute error between the accelerated verification test and the reliability analysis is only 1.1%; regarding average life, the relative error between the accelerated verification test and the reliability analysis is only 3.3%.

[0232] In the embodiment of the present application, the reliability life parameters of the motor driving mechanism are verified by using point estimation method and interval estimation method, and the results accuracy of the aforementioned reliability theoretical analysis method provided by the present application is well proved by the accelerated verification test of the motor driving mechanism, which indirectly proves the effectiveness and accuracy of the reliability accelerated verification method of the motor driving mechanism of the seeding machine proposed in the embodiment of the present application.

[0233] The reliability accelerated verification method of the motor driving structure of the seeding machine provided in the embodiment of the present application considers the load condition of the actual field working condition of the seeding machine, uses the correlation between the mechanical vibration generated during the field operation of the seeding machine and the temperature rise of the motor driving mechanism, collects the temperature information of multiple motor driving mechanisms under each preset working condition, calculates the vibration influence parameters corresponding to each motor driving mechanism, corrects the acceleration coefficient of the accelerated verification test through the vibration influence parameters, obtains the acceleration coefficient corresponding to each preset working condition, and verifies the reliability life parameters of the motor driving mechanism through the acceleration coefficient corresponding to each preset working condition, so that the reliability accelerated verification of the motor driving mechanism of the precision seed metering device can be realized, the operation process is simple, and the implementation cost is low.

[0234] The reliability accelerated verification device of the motor driving mechanism of the seeding machine provided in the present application is described below, and the reliability accelerated verification device of the motor driving mechanism of the seeding machine described below can be correspondingly referred to the reliability accelerated verification method of the motor driving mechanism of the seeding machine described above.

[0235] Figure 7 is a structural schematic diagram of the reliability accelerated verification device of the motor driving mechanism of the seeding machine provided in the present application, as Figure 7 shown, comprising:

[0236] The first processing module 710 is configured to determine the vibration influence parameters corresponding to each motor driving mechanism based on the collected temperature information of multiple motor driving mechanisms under each preset working condition; the vibration influence parameters are used to represent the relationship between the mechanical vibration generated during the operation of the seeding machine and the temperature rise of the motor driving mechanism thereof;

[0237] The second processing module 720 is configured to determine the acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter;

[0238] The first verification module 730 is configured to verify the reliability life parameters of the motor driving mechanism based on each acceleration coefficient;

[0239] The reliability life parameters are obtained by analyzing the fault tree model of the motor driving mechanism; the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0240] The reliability accelerated verification device of the seeder motor driving mechanism can be used to execute the reliability accelerated verification method of the seeder motor driving mechanism, and has similar principles and technical effects, which will not be described here.

[0241] The reliability accelerated verification method of the seeder motor driving mechanism provided by the embodiment of the present application can consider the load condition of the actual field working condition of the seeder, utilize the correlation between the mechanical vibration generated during the field operation of the seeder and the temperature rise of the motor driving mechanism, collect the temperature information of a plurality of motor driving mechanisms under each preset working condition, calculate the vibration influence parameters corresponding to each motor driving mechanism, correct the acceleration coefficient of the accelerated verification test through the vibration influence parameters, obtain the acceleration coefficient corresponding to each preset working condition, and accelerate the reliability life parameters of the motor driving mechanism through the acceleration coefficient corresponding to each preset working condition, so that the reliability accelerated verification of the motor driving mechanism of the precision seed metering device can be realized, the operation process is simple, and the implementation cost is low.

[0242] Figure 8 is the schematic diagram of the physical structure of the electronic device provided by the present application, as shown in Figure 8 The electronic device can include a processor 810, a communications interface 820, a memory 830 and a communications bus 840, wherein the processor 810, the communications interface 820 and the memory 830 complete mutual communication through the communications bus 840. The processor 810 can call the logical instructions in the memory 830 to execute the reliability accelerated verification method of the seeder motor driving mechanism provided by each method, and the method includes: determining the vibration influence parameters corresponding to each motor driving mechanism based on the collected temperature information of a plurality of motor driving mechanisms under each preset working condition; the vibration influence parameters are used to represent the relationship between the mechanical vibration generated during the operation of the seeder and the temperature rise of the motor driving mechanism thereof; determining the acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter; verifying the reliability life parameters of the motor driving mechanism based on each acceleration coefficient; the reliability life parameters are obtained by analyzing the fault tree model of the motor driving mechanism; and the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0243] In addition, the logical instructions in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0244] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the reliability accelerated verification method of the motor driving mechanism of the seeding machine provided by the above-mentioned method, the method comprises: determining the vibration influence parameter corresponding to each motor driving mechanism based on the temperature information of the plurality of motor driving mechanisms under each preset working condition; the vibration influence parameter is used to represent the relationship between the mechanical vibration generated during the operation of the seeding machine and the temperature rise generated by the motor driving mechanism thereof; determining the acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter; verifying the reliability life parameter of the motor driving mechanism based on each acceleration coefficient; the reliability life parameter is obtained by analyzing the fault tree model of the motor driving mechanism; and the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0245] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the method for accelerated reliability verification of a motor driving mechanism of a seeding machine provided by any of the above methods. The method comprises: determining vibration influence parameters of each motor driving mechanism based on temperature information of the motor driving mechanisms collected under each preset working condition; the vibration influence parameters are used to represent the relationship between mechanical vibration generated during operation of the seeding machine and temperature rise generated by the motor driving mechanism; determining an acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter; verifying a reliability life parameter of the motor driving mechanism based on each acceleration coefficient; the reliability life parameter is obtained by analyzing a fault tree model of the motor driving mechanism; and the fault tree model is constructed based on various fault information of the motor driving mechanism.

[0246] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0247] From the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the method described in each embodiment or some part of the embodiment.

[0248] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of reliability accelerated validation of a planter motor drive mechanism, comprising: The method comprises the following steps: determining vibration influence parameters of each motor driving mechanism based on collected temperature information of the motor driving mechanisms under each preset working condition; the vibration influence parameters are used to represent the relationship between mechanical vibration generated during sowing machine operation and temperature rise generated by the motor driving mechanism; determining an acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter; verifying the reliability life parameters of the motor driving mechanism based on each acceleration coefficient; the reliability life parameters are obtained by analyzing a fault tree model of the motor driving mechanism; the fault tree model is constructed based on various fault information of the motor driving mechanism; the temperature information includes a first working condition temperature and a second working condition temperature; the first working condition temperature is the steady-state temperature of the motor under static condition, and the second working condition temperature is the steady-state temperature of the motor under working condition; determining the vibration influence parameters of each motor driving mechanism based on the collected temperature information of the motor driving mechanisms under each preset working condition, comprising: determining the working condition temperature difference of each motor driving mechanism based on the first working condition temperature and the second working condition temperature of each motor driving mechanism; obtaining the vibration influence parameters of each motor driving mechanism based on the ratio of the working condition temperature difference of each motor driving mechanism to the corresponding environmental temperature; determining the acceleration coefficient corresponding to each preset working condition based on each vibration influence parameter, comprising: obtaining the environmental temperature of each motor driving mechanism under the acceleration verification condition, and determining the corrected environmental temperature of each motor driving mechanism based on each vibration influence parameter and the environmental temperature under the corresponding acceleration verification condition; obtaining the temperature set information of each motor driving mechanism, and determining the acceleration coefficient corresponding to each preset working condition based on the temperature set information of each motor driving mechanism and the corresponding corrected environmental temperature; the temperature set information includes the environmental temperature under working condition, the temperature rise information of the motor under working condition, the temperature rise information of the motor under acceleration verification condition, and the motor winding temperature compensation empirical value.

2. The reliability accelerated validation method of planter motor drive mechanisms of claim 1, wherein, The motor driving mechanism comprises a motor and a Hall encoder assembly; before determining the vibration influence parameters of each motor driving mechanism based on the collected temperature information of the motor driving mechanisms under each preset working condition, the method further comprises: calling the fault tree model of the motor driving mechanism; the fault tree model is constructed based on various fault information of the motor and the Hall encoder assembly; determining the total failure rate of the motor and the total failure rate of the Hall encoder assembly based on the fault tree model; determining the average life of the motor driving mechanism, and the importance of the motor and the Hall encoder assembly based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly; the reliability life parameters of the motor driving mechanism include the average life of the motor driving mechanism, and the importance of the motor and the Hall encoder assembly.

3. The reliability accelerated validation method of planter motor drive mechanisms of claim 2, wherein, The average service life of the motor driving mechanism is determined based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly, comprising: The total failure rate of the motor driving mechanism is obtained based on the total failure rate of the motor and the total failure rate of the Hall encoder assembly; The reliability of the motor driving mechanism is determined based on a reliability theory model; The average service life of the motor driving mechanism is determined based on the total failure rate of the motor driving mechanism and the reliability of the motor driving mechanism.

4. The reliability accelerated validation method of a planter motor drive mechanism of any of claims 1-3, wherein, The reliability life parameters of the motor driving mechanism are verified based on each of the acceleration factors, comprising: The end time of each test under the acceleration factor corresponding to each preset working condition is determined; The importance parameter of the motor driving mechanism is determined according to the number of failed motor driving mechanisms in the acceleration verification test; The average service life estimation of the motor driving mechanism under each preset working condition is determined according to the end time of each test under the acceleration factor corresponding to each preset working condition by using point estimation method and interval estimation method; The reliability life parameters of the motor driving mechanism are verified by using the importance parameter of the motor driving mechanism and the average service life estimation of the motor driving mechanism.

5. A reliability accelerated validation device for a planter motor drive mechanism, comprising: Comprise: The first processing module is used for determining the vibration influence parameter corresponding to each motor driving mechanism based on the temperature information of a plurality of motor driving mechanisms under each preset working condition; the vibration influence parameter is used to represent the relationship between the mechanical vibration generated during the operation of the seeding machine and the temperature rise generated by the motor driving mechanism thereof; The second processing module is used for determining the acceleration factor corresponding to each preset working condition based on each vibration influence parameter; The first verification module is used for verifying the reliability life parameters of the motor driving mechanism based on each acceleration factor; The reliability life parameters are obtained by analyzing the fault tree model of the motor driving mechanism; the fault tree model is constructed based on various fault information of the motor driving mechanism; The temperature information comprises a first working condition temperature and a second working condition temperature; the first working condition temperature is the steady-state temperature of the motor under static condition, and the second working condition temperature is the steady-state temperature of the motor under working condition; The vibration influence parameter corresponding to each motor driving mechanism is determined based on the first working condition temperature and the second working condition temperature of each motor driving mechanism, comprising: The working condition temperature difference of each motor driving mechanism is determined based on the first working condition temperature and the second working condition temperature thereof; The vibration influence parameter corresponding to each motor driving mechanism is obtained based on the ratio of the working condition temperature difference of each motor driving mechanism to the corresponding ambient temperature; The acceleration factor corresponding to each preset working condition is determined based on each vibration influence parameter, comprising: The ambient temperature of each motor driving mechanism under the acceleration verification condition is obtained, and the corrected ambient temperature corresponding to each motor driving mechanism is determined based on each vibration influence parameter and the ambient temperature under the corresponding acceleration verification condition; Obtain temperature set information of each motor driving mechanism, and determine an acceleration coefficient corresponding to each preset working condition based on the temperature set information of each motor driving mechanism and the corresponding corrected ambient temperature; the temperature set information includes an ambient temperature under a working condition, temperature rise information of a motor under the working condition, temperature rise information of the motor under an acceleration verification condition, and motor winding temperature compensation empirical values.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine according to any one of claims 1 to 4 when executing the program.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine according to any one of claims 1 to 4 when executed by the processor.

8. A computer program product comprising a computer program, characterized in that, The computer program implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine according to any one of claims 1 to 4 when executed by the processor. The computer program implements the reliability acceleration verification method of the motor driving mechanism of the seeding machine according to any one of claims 1 to 4 when executed by the processor.

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