Speed reducer assembling system and method

Abstract

The invention relates to a speed reducer assembly system and method, and belongs to the technical field of intelligent assemblation.The method comprises the steps that multi-dimensional performance data of a speed reducer are collected; the pre-constructed intelligent assembly model calculates assembly evaluation indexes and / or an average value of the assembly evaluation indexes based on the multi-dimensional performance data; the intelligent assembly model judges whether the assembly is qualified or not based on a comparison result of an average value of the assembly evaluation indexes and a preset target value score; wherein a comprehensive performance evaluation function model based on weight distribution is arranged in the intelligent assembly model so as to calculate the assembly evaluation indexes and / or the average value of the assembly evaluation indexes. According to the intelligent assembly model provided by the invention, the dependence on manual judgment is reduced, and the automatic evaluation of the overall performance of the speed reducer is realized. The method provides a basis for quantitative evaluation for different application scenes, and solves the problem that a traditional test means cannot provide comprehensive performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent assembly, and in particular to a reducer assembly system and method. Background Art

[0002] Precision reducers are key components for achieving high-precision control and are widely used in industrial automation, robotics, aerospace and other fields. Their performance directly affects the operating accuracy and stability of the equipment. These fields have different requirements for reducers. For example, in industrial automation, efficiency and noise control are more emphasized, while in robotics, accuracy and vibration suppression are emphasized.

[0003] Although the existing assembly test methods can provide specific parameters such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise, the comprehensive evaluation of the overall performance of the reducer still needs to rely on manual work. This method has obvious limitations, that is, it is difficult to quickly adjust and optimize the performance of reducers that fail to meet the standards of specific application requirements. For example, in scenarios with high performance requirements, even a small return error may affect the overall performance of the equipment.

[0004] In order to overcome these problems, the existing technology has proposed an intelligent testing and evaluation system. The system should be able to automatically analyze the test data and quickly locate the specific reasons why the reducer performance does not meet the standards, so as to implement targeted improvement measures. This not only helps to improve production efficiency, but also ensures the high quality standards of the final product and meets the strict requirements in different application scenarios. In this way, the continuous development and technological progress of various industries can be more effectively supported.

[0005] In different application scenarios, the performance index requirements of precision reducers have different focuses, such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise. However, there is currently a lack of a method that can comprehensively evaluate these key performance indicators, making it difficult to obtain a comprehensive quantitative evaluation of the assembled reducer. Although existing testing methods can provide independent data such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise, these data have not been integrated into a unified evaluation system, resulting in the evaluation of the overall performance of the reducer still relying on manual judgment.

[0006] For example, CN117870545A discloses an analysis system for the assembly of motor reducers for industrial robot arms, including: a drive control system, a measurement system and a test platform. The drive control system can communicate with the measurement system to collect data during the test process; the drive control system further controls and communicates with the test platform to control the posture of the test platform, thereby adjusting the state of the motor under test and the reducer under test. Among them, the drive control system includes: a controller, a connecting line and a driver; the measurement system includes: a laser centering instrument and a three-dimensional force sensor; the test platform includes: a base, a parallel platform, a motor mounting plate, a transparent bellows and a reducer mounting plate. This technical solution is used for the analysis system of the assembly of motor reducers for industrial robot arms, and is used for data collection and testing of the motor under test and the reducer under test in different assembly states. However, this technical solution can only measure the posture and current data of the reducer, and cannot collect multi-performance data in an all-round way.

[0007] Since there is no systematic method to directly link various performance indicators with assembly parameters, it is more difficult to make efficient and fast assembly adjustments for reducers that fail to meet specific requirements. The ideal solution is to provide an analysis method that can assign weights to the above key performance indicators and use intelligent fusion technology to quantitatively evaluate the performance of reducers under different requirements. This will help technicians quickly determine the best assembly adjustment plan based on specific application requirements, thereby improving work efficiency and product quality.

[0008] Therefore, in order to overcome the limitations of existing technologies, there is a need for an intelligent assembly system for reducers that can intelligently integrate a comprehensive evaluation system of multiple performance indicators to support efficient and rapid assembly and adjustment of reducers. This can not only reduce human intervention, but also ensure that the reducer can achieve optimal performance in various application environments.

[0009] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0010] In different application scenarios, the performance index requirements of precision reducers have different focuses, such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise. However, there is currently a lack of a method that can comprehensively evaluate these key performance indicators, making it difficult to obtain a comprehensive quantitative evaluation of the assembled reducer. Although existing testing methods can provide independent data such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise, these data have not been integrated into a unified evaluation system, resulting in the evaluation of the overall performance of the reducer still relying on manual judgment.

[0011] How to improve the intelligent assembly method to reduce the reliance on manual labor in evaluating the overall performance of the reducer is a technical problem to be solved by the present invention.

[0012] In view of the deficiencies in the prior art, the present invention provides a reducer assembly method from a first aspect, the method comprising: collecting multi-dimensional performance data of the reducer; a pre-built intelligent assembly model calculates assembly evaluation indicators and / or the average value of the assembly evaluation indicators based on the multi-dimensional performance data; the intelligent assembly model determines whether the assembly is qualified based on a comparison result between the average value of the assembly evaluation indicators and a pre-set target value score; wherein a comprehensive performance evaluation function model based on weight distribution is provided in the intelligent assembly model to calculate the assembly evaluation indicators and / or the average value of the assembly evaluation indicators.

[0013] The intelligent assembly model provided by the present invention reduces the reliance on manual judgment and realizes the automatic evaluation of the overall performance of the reducer. It provides a basis for quantitative evaluation for different application scenarios and solves the problem that traditional testing methods cannot provide comprehensive performance evaluation.

[0014] According to a preferred embodiment, the step of constructing an intelligent assembly model includes: constructing a comprehensive performance evaluation function model based on weight distribution based on multi-dimensional performance indicators, and determining the weight coefficient in the comprehensive performance evaluation function model, wherein a judgment matrix is ​​constructed based on the relative scale value of the multi-dimensional performance data, and the weight coefficient is calculated; after determining the weight coefficient, the assembly evaluation index of the reducer is calculated based on the comprehensive performance evaluation function model; data enhancement is performed on the curve of the assembly evaluation index and a data set sample is formed; artificial intelligence model training is performed based on the data set sample to form an intelligent assembly model.

[0015] The present invention can adjust the importance of each performance index according to different application scenarios, ensuring that the final assembly evaluation index can accurately reflect the actual working conditions and requirements of the reducer.

[0016] According to a preferred embodiment, the method further includes: before determining the weight coefficient, normalizing the multi-dimensional performance data to limit the range of the original multi-dimensional performance data to [0,1], so that the convergence in the training process of the artificial intelligence model is faster. Through normalization, it is possible to avoid certain high-level data from having too much impact on the model during the training process, thereby ensuring the stability of the training process and accelerating the convergence of the model.

[0017] According to a preferred embodiment, the comprehensive performance evaluation function model is: α=ω1ET+ω2η+ω3S+ω4DB; wherein α represents the assembly evaluation index, ET represents the transmission error, η represents the transmission efficiency, S represents the vibration signal, DB represents the noise signal, ω1, ω2, ω3 and ω4 represent the weight coefficients respectively. The comprehensive performance evaluation function model constructed by the present invention enables each multi-dimensional performance index (transmission error, transmission efficiency, vibration signal, noise signal) to be assigned different weights according to actual needs, forming a comprehensive assembly evaluation index. This method not only takes into account the influence of a single performance parameter, but also takes into account the interaction between them, providing a more accurate measurement standard for the overall performance of the reducer.

[0018] According to a preferred embodiment, the step of determining the weight coefficient of the comprehensive performance evaluation function model includes: calculating the nth power of each row in the judgment matrix to obtain an n-dimensional vector: ; Normalize the vector into a weight vector, the weight coefficient is: ;in, represents the weight coefficient, Represents the relative scale value in the judgment matrix; Represents the nth root of the product of all elements in the j-th row of the judgment matrix.

[0019] The calculation method of the weight coefficient of the present invention ensures the scientificity and rationality of the weight distribution. This method can dynamically adjust the weight of each performance parameter according to the actual situation, so that the final assembly evaluation index can better reflect the real state of the reducer, and also enhances the flexibility and adaptability of the evaluation system.

[0020] According to a preferred embodiment, the step of performing data enhancement on the curve of the assembly evaluation index includes: performing data enhancement on the curve of the assembly evaluation index to calculate: ; ; ; f represents the signal data obtained after enhancement; p represents slicing of the assembly evaluation index α, q represents offsetting of the assembly evaluation index α, r represents front-to-back mirror flipping of the assembly evaluation index α, and α represents the assembly evaluation index; q a Indicates the starting point offset; p a Indicates the slice length; pT Indicates the number of signal sampling points when the output end of the reducer rotates for one cycle.

[0021] By performing data augmentation operations on the curves of the assembly evaluation index, the quantity and diversity of sample data are increased, which helps to improve the learning and generalization capabilities of the artificial intelligence model. This is critical to improving the performance of the model when facing new data, and further enhances the reliability and accuracy of the model's prediction results.

[0022] The present invention provides a reducer assembly system from a second aspect, comprising a sensing unit and a server. The sensing unit is used to collect multi-dimensional performance data of the reducer; an intelligent assembly model is pre-built inside the server. The intelligent assembly model calculates assembly evaluation indicators and / or the average value of assembly evaluation indicators based on the multi-dimensional performance data; and determines whether the assembly is qualified based on the comparison result of the average value of the assembly evaluation indicators and the preset target value score; wherein a comprehensive performance evaluation function model based on weight distribution is set in the intelligent assembly model to calculate the assembly evaluation indicators and / or the average value of the assembly evaluation indicators.

[0023] The advantages of the system of the present invention are that it realizes the automatic evaluation of the overall performance of the reducer, reduces the need for manual intervention, and improves the assembly efficiency and accuracy. At the same time, by introducing a comprehensive performance evaluation function model based on weight distribution, the different emphases on the performance of the reducer in different application scenarios are fully considered, thereby ensuring the comprehensiveness and accuracy of the assembly qualification analysis.

[0024] According to a preferred embodiment, the intelligent assembly model is configured as follows: constructing a comprehensive performance evaluation function model based on weight distribution based on multi-dimensional performance indicators, and determining the weight coefficients in the comprehensive performance evaluation function model, wherein a judgment matrix is ​​constructed based on the relative scale values ​​of the multi-dimensional performance data, and the weight coefficients are calculated; after determining the weight coefficients, calculating the assembly evaluation indicators of the reducer based on the comprehensive performance evaluation function model.

[0025] The intelligent assembly model allows the weights of different performance parameters to be adjusted dynamically according to their importance to better reflect the actual working conditions of the reducer. This not only improves the scientificity and rationality of the assembly evaluation indicators, but also enhances the adaptability and flexibility of the system. By constructing a judgment matrix and calculating the weight coefficient, it can ensure that the weight distribution of each performance parameter is more reasonable, thereby improving the accuracy and reliability of the assembly qualification analysis.

[0026] According to a preferred embodiment, the intelligent assembly model is also configured to: normalize the multi-dimensional performance data before determining the weight coefficient to limit the range of the original multi-dimensional performance data to [0,1], so that the convergence in the process of training the artificial intelligence model is faster.

[0027] Normalization ensures that data of different magnitudes will not have a disproportionate impact on the final result, while also accelerating the convergence speed of the AI ​​model training process and improving the stability and accuracy of the intelligent assembly model. This can improve the overall performance of the intelligent assembly model because it ensures that even in the face of complex and changing data sets, the intelligent assembly model can quickly learn and make reliable predictions, thereby optimizing the process of assembly qualification analysis.

[0028] According to a preferred embodiment, the comprehensive performance evaluation function model is: α=ω1ET+ω2η+ω3S+ω4DB; wherein α represents the assembly evaluation index, ET represents the transmission error, η represents the transmission efficiency, S represents the vibration signal, DB represents the noise signal, and ω1, ω2, ω3 and ω4 represent the weight coefficients respectively. The comprehensive performance evaluation function model helps to comprehensively quantify the key performance indicators of the reducer and provides a unified evaluation system. By reasonably setting the weight coefficients, the assembly evaluation standards can be customized according to different application requirements to ensure that the evaluation results have both wide applicability and accuracy. This not only simplifies the process of assembly qualification analysis, but also improves the reliability and consistency of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a simplified schematic diagram of the module connection relationship of the reducer assembly system provided by the present invention;

[0030] Figure 2 It is a schematic flow chart of the reducer assembly method provided by the present invention;

[0031] Figure 3 It is a logical schematic diagram of the evaluation and correction guidance of the reducer assembly system provided by the present invention;

[0032] Figure 4 It is a schematic diagram of the connection structure of the intelligent assembly test platform for the reducer provided by the present invention.

[0033] List of reference numerals:

[0034] 100: intelligent assembly test platform; 110: driving motor; 120: first torque sensor; 130: input end encoder; 140: reducer; 150: output end encoder; 160: second torque sensor; 170: load motor; 180: server; 190: noise sensor; 200: vibration sensor. DETAILED DESCRIPTION

[0035] The following is a detailed description with reference to the accompanying drawings.

[0036] The intelligent testing and evaluation system provided by the prior art has the ability to automatically analyze test data and quickly identify the exact cause of the poor performance of the reducer 140, thereby implementing accurate improvement strategies. This not only helps to improve manufacturing efficiency, but also ensures that the final product meets high standards of quality requirements and adapts to strict specifications in various application scenarios.

[0037] However, for different application environments, the requirements for various performance indicators of the reducer 140, such as transmission accuracy, transmission efficiency, vibration level and noise level, are different. However, there is currently no method for comprehensively evaluating these important performance indicators, which makes it difficult to obtain a comprehensive and accurate evaluation of the assembled reducer 140. Although existing testing tools can provide separate data such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise, this information has not been integrated into a unified evaluation framework, so the evaluation of the overall performance of the reducer 140 still relies on manual analysis.

[0038] Due to the lack of an effective method to directly link various performance indicators with assembly parameters, it is difficult to efficiently and quickly adjust the reducer 140 that does not meet specific requirements.

[0039] In view of the deficiencies of the prior art, the present invention provides a reducer assembly system and method. The present invention can also provide an intelligent assembly test platform 100 for a reducer. The present invention can also provide a server 180, which is used to process the data of the reducer 140 and evaluate whether the assembly is qualified. Preferably, the server 180 is also used to analyze the fault of the reducer 140 and provide corresponding correction solutions. Preferably, the present invention provides a storage medium, which is used to store the operating program of the server 180.

[0040] Preferably, the server 180 can also be replaced by physical hardware such as a dedicated integrated chip, a remote server, a server group, etc. that has data processing capabilities or runs an artificial intelligence model.

[0041] like Figure 1 and Figure 4 As shown, the present invention provides a reducer assembly system, including a sensing unit and a server 180.

[0042] The sensor unit is used to collect multi-dimensional performance data of the reducer 140. Figure 1As shown, the sensing unit includes a first torque sensor 120, a second torque sensor 160, a noise sensor 190 and a vibration sensor 200. Preferably, the first torque sensor 120 is connected to the input end of the reducer 140 through the input end encoder 130. The second torque sensor 160 is connected to the output end of the reducer 140 through the output end encoder 150. The first torque sensor 120 and the second torque sensor 160 are used to collect position data and torsional force data of the reducer 140 to analyze the transmission accuracy and transmission efficiency of the reducer 140. The noise sensor 190 is used to collect noise data of the reducer 140 for transmission noise analysis of the reducer 140. The vibration sensor 200 is used to collect vibration data of the reducer 140 for transmission vibration analysis of the reducer 140. Preferably, during the collection, the vibration sensor 200 is connected to the assembled reducer 140 through a dedicated hole.

[0043] The present invention can also provide an intelligent assembly test platform 100, such as Figure 4 As shown. On the intelligent assembly test platform 100, the first torque sensor 120 is connected to the drive motor 110. The second torque sensor 160 is connected to the load motor 170. Each sensor in the sensing unit is connected to the server 180 by wire or wirelessly for data transmission. In other words, the multi-dimensional performance data of the reducer 140 of the present invention includes position data (angle), torsional force data (torque), noise data and vibration data.

[0044] Preferably, if Figure 2 As shown, various sensor detection and experimental data collection are carried out.

[0045] Specifically, before the assembly begins, experimental data of known fault types of the reducer 140 are collected. Specifically, the sensor unit on the intelligent assembly test platform 100 detects signals such as angle, torque, vibration, and noise of the assembled finished product of the reducer 140. That is, before the test, the reducer 140 of known error types is tested, and the angle, torque, vibration, and noise signals are detected by the sensor unit.

[0046] Preferably, the server 180 has a pre-built intelligent assembly model inside. The construction principle of the intelligent assembly model is: by processing the four signals of angle signal, torque signal, vibration signal and noise signal, the corresponding four analysis indicators are calculated and normalized to obtain the assembly evaluation indicator α=ω1ε+ω2η+ω3S+ω4DB. The assembly evaluation indicator is used as a data set, and an artificial intelligence model such as a convolutional neural network model is trained through deep learning to realize the identification of the fault types of different reducers 140. The artificial intelligence model formed by training is an intelligent assembly model. Figure 3As shown, the four analysis indicators are: transmission accuracy analysis indicator, transmission efficiency analysis indicator, transmission vibration analysis indicator and transmission noise analysis indicator.

[0047] The smart assembly model is configured as:

[0048] S1: Calculate assembly evaluation indicators and / or average values ​​of assembly evaluation indicators based on multi-dimensional performance data.

[0049] Preferably, a comprehensive performance evaluation function model based on weight distribution is provided in the intelligent assembly model to calculate the assembly evaluation index and / or the average value of the assembly evaluation index.

[0050] S2: Determine whether the assembly is qualified based on the comparison result between the average value of the assembly evaluation index and the preset target value score.

[0051] Preferably, the steps of constructing the intelligent assembly model include:

[0052] S31: Construct a comprehensive performance evaluation function model based on weight distribution based on multi-dimensional performance indicators, and determine the weight coefficients in the comprehensive performance evaluation function model.

[0053] According to a preferred embodiment, the comprehensive performance evaluation function model is:

[0054] α=ω1ET+ω2η+ω3S+ω4DB.

[0055] Wherein, α represents the assembly evaluation index, ET represents the transmission error, η represents the transmission efficiency, S represents the vibration signal, DB represents the noise signal, ω1, ω2, ω3 and ω4 represent weight coefficients respectively. The vibration signal S and the noise signal DB are directly collected by the vibration sensor 200 and the noise sensor 190 respectively.

[0056] Preferably, the transmission accuracy of this type is analyzed based on the angle test data collected by the first torque sensor 120 and the second torque sensor 160 .

[0057] The calculation formula of transmission error ET is:

[0058] ET(t)=θ2(t)-θ1(t) / i.

[0059] θ1 represents the angular displacement of the input shaft, θ2 represents the angular displacement of the output shaft, and i represents the theoretical transmission ratio, that is, the designed speed ratio between the input shaft and the output shaft.

[0060] Preferably, the transmission efficiency is analyzed based on the input / output torque test data collected by the first torque sensor 120 and the second torque sensor 160 .

[0061] The calculation formula of transmission efficiency η is: η=(T out / T in )×100%.

[0062] T out T represents the torque applied to the output end by the reducer 140; in Represents the torque applied to the input.

[0063] Preferably, a storage unit is provided in the server 180. The vibration and noise data collected by the vibration sensor 200 and the noise sensor 190 are directly stored in the storage unit of the server 180 for subsequent intelligent assembly model call.

[0064] Preferably, before determining the weight coefficient, the multidimensional performance data is normalized to limit the range of the original multidimensional performance data to [0,1], so that the convergence in the training process of the artificial intelligence model is faster, the performance of the intelligent assembly model is improved, and the dimensional expression is converted into a dimensionless expression, so that indicators of different units or magnitudes can be substituted into the assembly evaluation index function to evaluate and analyze the assembly of the reducer 140.

[0065] Preferably, the normalized calculation formula is:

[0066] X=(X i -X min ) / (X max -X min ).

[0067] Among them, X represents the normalized data, X i Represents the original multi-dimensional performance data. i It includes four types of data: transmission error ET, transmission efficiency η, vibration signal S and noise signal DB. max With X min Respectively represent the maximum and minimum values ​​of the original multi-dimensional performance data.

[0068] Preferably, it is necessary to determine the weight coefficients corresponding to the transmission accuracy, transmission efficiency, vibration data and noise data of the reducer 140, respectively, and the comprehensive performance evaluation function model of the reducer 140 can be weighted and balanced according to the reducer data indicators with different requirements.

[0069] Preferably, a judgment matrix is ​​constructed based on relative scale values ​​of multi-dimensional performance data, and weight coefficients are calculated.

[0070] S311: Construct a weight hierarchy model of the reducer 140 .

[0071] The target layer is the assembled reducer 140, the criterion layer is the transmission accuracy, transmission efficiency, vibration data and noise data of the assembled reducer 140, and the solution layer is the assembled qualified reducer 140 and the assembled unqualified reducer 140.

[0072] Preferably, the AHP method can systematize the complex assembly process and decompose it into multiple hierarchical structures: the highest level is the target level, such as the overall performance of the qualified reducer 140; the middle level is the criterion level, covering data such as transmission accuracy, transmission efficiency, transmission vibration and transmission noise; the lowest level is the solution level or indicator level, which specifically involves the technical parameters of the reducer 140. This hierarchical structure helps to clearly display and process the relationship between the various analysis indicators, ensuring that no key factors are overlooked in the decision-making process.

[0073] By considering the assembly process of the reducer 140 as a system and subdividing it into different levels and criteria, the AHP method can comprehensively consider the relationship between various assembly indicators. This method provides a systematic evaluation framework, which is particularly suitable for complex system assembly such as the reducer 140, because it can ensure the balance between various performance analysis indicators. Such a systematic evaluation not only improves the transparency of the assembly process, but also enhances the control ability of the final assembly quality.

[0074] S312: Construct a judgment matrix based on the relative importance of indicators.

[0075] The scale range of the judgment matrix is ​​integers 1-9 and the reciprocal of 1-9. The value indicates the importance of two indicators among transmission accuracy, transmission efficiency, vibration level and noise level. The larger the scale, the higher the importance of the former, and a scale of 1 indicates that they are equally important.

[0076] Construct an n×n judgment matrix P:

[0077] .

[0078] a ij Indicates relative scale value. ij Satisfy the following conditions: (1) a ij >0, (2)a ij =1 / a ji , (3) a ii ,a jj =0.

[0079] There are 4 criteria layer indicators, so n=4.

[0080] For example, construct a 4×4 judgment matrix P:

[0081] .

[0082] For example, when assembling a harmonic reducer, there are requirements for high transmission accuracy, high transmission efficiency, relatively good vibration level and average noise level. The intelligent assembly model can clearly obtain the scale between these four performance indicators of the harmonic reducer required for assembly. The scale of transmission accuracy to transmission efficiency is 1, the scale of transmission accuracy to vibration level is 5, and the scale of transmission accuracy to noise level is 9. According to Satty's 1-9 scale, all relative scale values ​​can be obtained. ij .

[0083] It can be seen that the judgment matrix will change accordingly according to the user's performance requirements.

[0084] S313: Calculate the weight coefficient based on the square root method.

[0085] Calculate the nth power of each row in the judgment matrix to obtain an n-dimensional vector: . Normalize the vector into a weight vector, and the weight coefficient is: .in, represents the weight coefficient, Represents the relative scale value in the judgment matrix; Represents the nth root of the product of all elements in the j-th row of the judgment matrix.

[0086] For example, calculate the fourth power of each row in the judgment matrix to obtain a 4-dimensional vector: . Normalize the vector into a weight vector, and the weight coefficient is: .

[0087] Preferably, whether the consistency of the matrix is ​​passed is evaluated based on the CR value.

[0088] .

[0089] CI stands for consistency ratio. RI value is the random consistency index obtained by Satty simulation 1000 times, which can be obtained by looking up the table.

[0090] First, calculate the maximum eigenvalue of the judgment matrix: .

[0091] For example, when n=4, .

[0092] (Pω) i It means the judgment matrix is ​​multiplied by the standardized weights and accumulated row by row.

[0093] Then, calculate the consistency ratio CI value: .

[0094] For example, when n=4, .

[0095] When CR<0.1, it means that the consistency of the judgment matrix is ​​considered to be within the allowable range.

[0096] When the CR value is ≥ 0.1, it means that there is a logical error in the judgment matrix.

[0097] For example: the scale of the transmission error ET relative to the vibration data S is 3 (indicating that the transmission accuracy is slightly more important than the transmission vibration), and the scale of the transmission error ET relative to the noise data DB is 1 / 3 (indicating that the transmission noise is slightly more important than the transmission accuracy). When judging the transmission accuracy relative to the transmission noise, according to the above logic, the transmission noise should be more important than the transmission vibration. If the scale of the vibration data S relative to the noise data DB is incorrectly filled in as 3 (transmission vibration is slightly more important than transmission noise) when constructing the judgment matrix, then a logical error is made.

[0098] When calculating the weight distribution, the present invention adopts the AHP hierarchical analysis method to calculate the weight coefficient corresponding to the analysis index. Its advantages are: no need for a large number of data samples to support, nor need to consider the mutual influence between data, only the required reducer 140 assembly requirements need to be considered, starting from the essence of the reducer performance requirements, and the logic is clearer than that of general quantitative methods. The hierarchical analysis method regards the research object as a system, and makes decisions according to the thinking mode of decomposition, comparison, judgment, and synthesis. It is particularly suitable for multi-objective and multi-criteria system evaluation such as reducer performance evaluation. The integration of the deep learning method of the intelligent assembly model of the present invention associates the data of multiple sensor units with the reducer performance indicators. While realizing the classification of assembly errors, the trained data set is combined with the weight distribution to realize intelligent assembly evaluation.

[0099] S32: After the weight coefficient is determined, the assembly evaluation index of the reducer 140 is calculated based on the comprehensive performance evaluation function model.

[0100] After calculating the weight coefficient ω according to actual needs, the weight coefficient is substituted into the comprehensive performance evaluation function model: α=ω1ε+ω2η+ω3S+ω4DB, and the assembly evaluation index α can be obtained as a curve of the synthetic data of the four original performance data.

[0101] Perform mean processing on α: .

[0102] represents the average value of the assembly evaluation index α, Represents the assembly evaluation index α of each dimension.

[0103] If the objects tested during the test are all reducers 140 of known fault types, there is no need to perform assembly qualification judgment.

[0104] S33: Perform data enhancement on the curve of the assembly evaluation index and form a data set sample.

[0105] After slicing, shifting, and flipping the assembly evaluation index α, the sample f is obtained.

[0106] Preferably, data enhancement is performed on the curve of the assembly evaluation index to calculate:

[0107] ;

[0108] ;

[0109] .

[0110] f represents the signal data obtained after enhancement; p represents slicing of the assembly evaluation index α, q represents offsetting of the assembly evaluation index α, r represents front-to-back mirror flipping of the assembly evaluation index α, and α represents the assembly evaluation index; q a Indicates the starting point offset; p a Indicates the slice length; p T Indicates the number of signal sampling points when the output end of the reducer rotates one circle, that is, one cycle.

[0111] It means that the slices at the corresponding positions are spliced ​​end to end in sequence to obtain the final signal data sample f.

[0112] S34: Perform artificial intelligence model training based on data set samples to form an intelligent assembly model.

[0113] The sample order is disrupted by random seeds. The sample library consisting of several signal data samples f is divided into training set, validation set and test set in a ratio of 7:2:1 to obtain the training data set for the artificial intelligence model.

[0114] like Figure 2 and Figure 3 As shown, when a deep learning neural network model is used as an artificial intelligence model, the deep learning neural network model performs deep learning tasks by processing data sets. The deep learning neural network model architecture includes components such as convolutional layers, pooling layers, ReLU activation functions, Dropout layers, fully connected layers, and Softmax layers, which work together on the data set to optimize parameters and extract data features. After completing the above steps and the model training is completed, the classification performance of the model is evaluated through the test set, thereby achieving accurate classification of different types of faults of the reducer 140, and finally obtaining an optimized pre-trained model, namely the intelligent assembly model.

[0115] Specifically, during the training process, the present invention selects the Adam optimizer as the optimization algorithm, which can effectively adjust the learning rate and accelerate the convergence process. At the same time, the cross entropy loss function is used as a standard to measure the difference between the predicted result and the actual label. In order to enhance the generalization ability of the model, a Dropout layer is set after the convolution layer to randomly discard a part of the neurons to reduce overfitting. Then, the Flatten layer is used to convert the multidimensional feature map into a one-dimensional vector to facilitate subsequent fully connected layer processing. Finally, the Softmax layer is responsible for calculating the probability distribution corresponding to each category and outputting the final classification result. In this way, the entire process not only improves the accuracy of the model in identifying the fault type of the reducer 140, but also ensures that the model has good generalization performance and is suitable for fault detection tasks in actual application scenarios.

[0116] After completing the training, verification and testing of the artificial intelligence model, the assembly of reducer 140 will be officially carried out.

[0117] After the reducer 140 is assembled, the intelligent assembly model performs an assembly evaluation index function α and its mean based on the multi-dimensional performance data collected by the sensor unit. Calculation.

[0118] In the intelligent assembly model, the model parameters, namely the target value scores, are set in advance.

[0119] The target value score ξ is set according to different performance requirements of the reducer 140 .

[0120] ,

[0121] Among them, E.T. Cv , η Cv , S Cv , DB Cv They respectively represent indicator data sets of the reducer 140 under standard operating conditions.

[0122] The following table lists the target value functions used to calculate the target value scores under different performance requirements.

[0123]

[0124] The intelligent assembly model determines whether the assembly is qualified based on the average value and target value score of the assembly evaluation index.

[0125] Preferably, when When the intelligent assembly model determines that the assembly is qualified, the product will be shipped out, otherwise it will be unqualified.

[0126] Preferably, when the classification result is unqualified, the intelligent assembly model classifies the test data of the reducer 140 and determines the classification of the error type. Specifically, the intelligent assembly model collects the data of the assembly evaluation index α and performs error type classification.

[0127] During the training of the artificial intelligence model, since the experimental data of the reducer 140 with faults of different error types is used, the intelligent assembly model can learn the error type during the training process, so that it can determine the classification of the error type and output the classification result of the error type.

[0128] Preferably, if Figure 3 As shown, after obtaining the classification result, the intelligent assembly model can now judge the error type of the reducer 140 assembly. If it is judged to be a gear processing accuracy error, it needs to be returned to the factory for reprocessing; if it is judged to be an assembly error, continue to classify different types of assembly errors. If it is judged to be an assembly center distance error, it is necessary to adjust the center distance between important meshing parts; if it is judged to be a bearing preload error, it is necessary to adjust the size of the bearing preload when the reducer 140 is installed.

[0129] After the reducer 140 is assembled, the intelligent assembly model proposes a corresponding correction plan for the assembled product of the reducer 140 according to the actual situation.

[0130] The intelligent assembly test platform 100 of the reducer 140 of the present invention and its intelligent assembly model realize accurate classification of error types by reconstructing the comprehensive performance evaluation function model and performing intelligent evaluation of the qualified situation of the assembly of the reducer 140, thereby providing targeted correction solutions and significantly improving the quality and production efficiency of the assembled finished products. In addition, with the accumulation of data and iterative optimization of the model, the predictive ability and robustness of the intelligent assembly model are continuously improved, which further promotes the continuous improvement of production, reduces the overall cost, and enhances the market competitiveness of the enterprise.

[0131] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" and "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. A method for assembling a reducer, characterized in that: The method comprises: Collecting multi-dimensional performance data of the reducer (140); The pre-built intelligent assembly model calculates the assembly evaluation index and / or the average value of the assembly evaluation index based on the multi-dimensional performance data; The intelligent assembly model determines whether the assembly is qualified based on the comparison result of the average value of the assembly evaluation index and the preset target value score; Wherein, a comprehensive performance evaluation function model based on weight distribution is provided in the intelligent assembly model to calculate the assembly evaluation index and / or the average value of the assembly evaluation index.

2. The method according to claim 1, characterized in that The steps of constructing the intelligent assembly model include: Based on multi-dimensional performance indicators, a comprehensive performance evaluation function model based on weight distribution is constructed. Determining a weight coefficient in the comprehensive performance evaluation function model, wherein a judgment matrix is ​​constructed based on relative scale values ​​of multi-dimensional performance data, and the weight coefficient is calculated; After determining the weight coefficient, calculating an assembly evaluation index of the reducer (140) based on the comprehensive performance evaluation function model; Performing data enhancement on the curve of the assembly evaluation index and forming a data set sample; Artificial intelligence model training is performed based on the data set samples to form an intelligent assembly model.

3. The method according to claim 2, characterized in that The method further comprises: Before determining the weight coefficient, the multi-dimensional performance data is normalized to limit the range of the original multi-dimensional performance data to [0, 1], so that the convergence in the process of training the artificial intelligence model is faster.

4. The method according to any one of claims 1 to 3, characterized in that The comprehensive performance evaluation function model is: α=ω1ET+ω2η+ω3S+ω4DB; Among them, α represents the assembly evaluation index, ET represents the transmission error, η represents the transmission efficiency, S represents the vibration signal, DB represents the noise signal, ω1, ω2, ω3 and ω4 represent the weight coefficients respectively.

5. The method according to claim 2 or 3, characterized in that: The step of determining the weight coefficient of the comprehensive performance evaluation function model comprises: Calculate the nth power of each row in the judgment matrix to obtain an n-dimensional vector: ; The vector is normalized into a weight vector, and the weight coefficient is: ; in, represents the weight coefficient, Represents the relative scale value in the judgment matrix; Represents the nth root of the product of all elements in the j-th row of the judgment matrix.

6. The method according to claim 2 or 3, characterized in that: The step of performing data enhancement on the curve of the assembly evaluation index comprises: The curve of the assembly evaluation index is calculated by data enhancement: ； ； ； f represents the signal data obtained after enhancement; p represents slicing of the assembly evaluation index α, q represents offsetting of the assembly evaluation index α, r represents front-to-back mirror flipping of the assembly evaluation index α, and α represents the assembly evaluation index; q a Indicates the starting point offset; p a Indicates the slice length; p T Indicates the number of signal sampling points when the output end of the reducer rotates for one cycle.

7. A reducer assembly system, characterized in that: include: A sensor unit for collecting multi-dimensional performance data of the reducer (140); The server (180) has a pre-built intelligent assembly model. The intelligent assembly model calculates the assembly evaluation index and / or the average value of the assembly evaluation index based on the multi-dimensional performance data; and determines whether the assembly is qualified based on the comparison result of the average value of the assembly evaluation index and a preset target value score; Wherein, a comprehensive performance evaluation function model based on weight distribution is provided in the intelligent assembly model to calculate the assembly evaluation index and / or the average value of the assembly evaluation index.

8. The system according to claim 7, characterized in that The intelligent assembly model is configured as follows: Based on multi-dimensional performance indicators, a comprehensive performance evaluation function model based on weight distribution is constructed. Determining a weight coefficient in the comprehensive performance evaluation function model, wherein a judgment matrix is ​​constructed based on relative scale values ​​of multi-dimensional performance data, and the weight coefficient is calculated; After the weight coefficient is determined, an assembly evaluation index of the reducer (140) is calculated based on the comprehensive performance evaluation function model.

9. The system according to claim 7 or 8, characterized in that: The intelligent assembly model is also configured as follows: Before determining the weight coefficient, the multi-dimensional performance data is normalized to limit the range of the original multi-dimensional performance data to [0, 1], so that the convergence in the process of training the artificial intelligence model is faster.

10. The system according to claim 9, characterized in that The comprehensive performance evaluation function model is: α=ω1ET+ω2η+ω3S+ω4DB; Among them, α represents the assembly evaluation index, ET represents the transmission error, η represents the transmission efficiency, S represents the vibration signal, DB represents the noise signal, ω1, ω2, ω3 and ω4 represent the weight coefficients respectively.

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