Gear pump valve core stress relaxation prediction method and performance degradation model data expansion method

Abstract

The present invention relates to a gear pump valve core stress relaxation prediction method and a performance degradation model data expansion method, which comprises the following steps: S1, selecting a test piece; S2, installing the test piece on a loading fixture and starting a stress test; S3, collecting stress values ​​at each time point; S4, calculating a load loss rate; S5, fitting a logarithmic regression model of the test piece using the least squares method; S6, determining parameter values ​​in the regression model; S7, calculating a stress prediction value; S8, calculating the error between the stress prediction value and the actual stress value; S9, repeating steps S2 to S8 to determine the optimal regression model of the test piece; S10, predicting the stress value of similar test pieces at any time point using the optimal regression model. The present invention more accurately fits stress relaxation data by introducing a degradation model and a regression model, and simultaneously expands subsequent numerical values ​​by adopting a systematic error analysis and model verification strategy, thereby significantly improving the performance degradation prediction accuracy of key components of a gear pump.

Description

Technical Field

[0001] The present invention relates to the technical field of performance degradation analysis of key components of a gear pump, and in particular to a gear pump valve core stress relaxation prediction method and a performance degradation model data expansion method. Background Art

[0002] Gear pumps are widely used in industrial automation, hydraulic transmission, and lubrication systems. Their reliability and service life directly impact the performance and stability of the entire system. As a core component of a gear pump, the life of the valve core is crucial to the overall quality of the pump. However, due to the complexity of the operating environment and the variability of operating conditions, accurately predicting the service life of the valve core is challenging. To understand the degradation process of gear pumps, traditional methods rely mainly on large-sample, long-term physical testing. This method is not only time-consuming but also lacks accuracy. In practical applications, changes in environmental and operating conditions can lead to reduced repeatability and reliability of experimental results. A trend in addressing this issue is to build predictive models that simulate the performance degradation of gear pumps under different operating conditions while conducting physical testing for a certain period of time. By utilizing simulation data, key factors affecting gear pump life can be identified, providing important reference for product design and optimization.

[0003] Therefore, in order to improve the design quality of gear pumps and extend their service life, the present invention proposes a gear pump valve core stress relaxation prediction method and a performance degradation model data expansion method to solve the above problems. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a method for predicting stress relaxation of the gear pump valve core. By using a logarithmic regression model of load loss rate and relaxation time, the stress relaxation test data can be fit more accurately, and the data can be used for data expansion of the performance degradation model of key components of the gear pump. Compared with traditional linear models, this method performs better in capturing nonlinear relaxation behavior and significantly improves the fitting accuracy. At the same time, a systematic error analysis and model verification strategy are adopted, so that the model can maintain a high prediction accuracy under different experimental conditions.

[0005] The technical solution adopted by the present invention is a method for predicting stress relaxation of a gear pump valve core, which comprises the following steps:

[0006] S1. Select a plurality of identical gear pump valve core test pieces to be tested, and divide them into a first group of gear pump valve core test pieces, a second group of gear pump valve core test pieces, and an Nth group of gear pump valve core test pieces;

[0007] S2. Install the selected gear pump valve core specimens onto a loading fixture in the stress relaxation testing machine, start the loading fixture, and begin stress testing of the first group of gear pump valve core specimens;

[0008] S3, using a data acquisition and processing module in the stress relaxation testing machine to collect in real time the stress values ​​of the gear pump valve core specimen measured at each time point during the loading time, and dividing the data measured during the loading time into a training phase data set and a test phase data set;

[0009] S4. Calculate the load loss rate of the gear pump valve core specimen at each time point during the training phase based on the training phase data set recorded in step S3. The expression is:

[0010]

[0011] Where, P0 represents the initial stress value of the gear pump valve core specimen, P t represents the stress value at time t;

[0012] S5. The least square method is used to fit the logarithmic regression model of the load loss rate and relaxation time of the gear pump valve core specimen, and the expression is:

[0013]

[0014] Where, Indicates the load loss rate, t R represents the relaxation time, A and B represent the parameters of the regression model;

[0015] S6. Determine the parameter values ​​of A and B in the regression model of the first group of gear pump valve core specimens based on the regression model in step S5;

[0016] S7. Calculate the stress prediction value of the first group of gear pump valve core specimens during the test phase based on the fitted regression model;

[0017] S8. Calculate the error between the predicted stress value of the first group of gear pump valve core specimens during the test phase and the actual stress value recorded in step S3 during the test phase, and obtain the root mean square difference to verify the fitting quality of the regression model;

[0018] S9, repeating steps S2 to S8, performing stress tests on the second group of gear pump valve core specimens and the Nth group of gear pump valve core specimens in sequence, and selecting the regression model of the group of gear pump valve core specimens with the smallest root mean square difference as the optimal regression model;

[0019] S10. Through the optimal regression model, the stress value of the gear pump valve core specimen of the same type at any time point can be predicted, thereby predicting the stress relaxation amount of the gear pump valve core specimen.

[0020] Furthermore, in step S2, the selected gear pump valve core specimen is mounted on a loading fixture in a stress relaxation testing machine, the loading fixture is started, and the stress test of the first group of gear pump valve core specimens is started. The specific steps include:

[0021] S21. Install the first set of gear pump valve core test pieces on the loading fixture and set the initial test parameters;

[0022] S22, raising the temperature of the insulated box to a predetermined test temperature and performing heat preservation;

[0023] S23, starting the loading push rod to apply a loading force to the first group of gear pump valve core test pieces so that the gear pump valve core test pieces maintain a certain elastic displacement;

[0024] S24, setting the loading temperature and loading time of the gear pump valve core test piece;

[0025] S25. Recording the elastic force generated by the gear pump valve core test piece at each time point in real time using a force sensor;

[0026] S26. When the elastic force of the gear pump valve core specimen decays to the loading time, the stress test of the first group of gear pump valve core specimens is stopped.

[0027] Preferably, the displacement control accuracy of the loading tool in step S23 is greater than or equal to ±0.1%.

[0028] Furthermore, the step S3 divides the data measured during the loading time into a training phase data set and a test phase data set, specifically including the following steps:

[0029] S31, performing equidistant sampling on the stress value data of the gear pump valve core test piece measured at each time point during the loading time, which is collected in real time;

[0030] S32, dividing the data after equal distance sampling into data sets;

[0031] S33, using 60% of the sampled data as a training phase data set, which can be used to fit the regression model of the gear pump valve core test piece;

[0032] S34. 40% of the sampled data is used as a test phase data set to test the error between the stress value predicted by the regression model and the actual stress value in the test phase.

[0033] Preferably, the sampling frequency of the data acquisition and processing module in step S3 is greater than or equal to 1 Hz.

[0034] A second aspect of the present invention provides a method for expanding data of a gear pump valve core performance degradation model, comprising the following steps:

[0035] S1. Select the gear pump valve core specimen to be tested;

[0036] S2. Install the selected gear pump valve core specimen on the loading fixture in the stress relaxation testing machine, start the loading fixture, and start the stress test of the gear pump valve core specimen;

[0037] S3, using a data acquisition and processing module in the stress relaxation testing machine to collect the initial stress value of the gear pump valve core specimen and the stress values ​​measured at each time point in real time, and storing the collected stress values ​​in the data acquisition and processing module;

[0038] S4, preprocessing the collected stress value data of the gear pump valve core test piece;

[0039] S5. Using a variety of fitting methods, fit the stress value data pre-processed in step S4;

[0040] S6. Calculate the error between the predicted value in each fitting method and the actual stress value in step S3 to verify the fitting quality of each fitting method;

[0041] S7. Based on the preset fitting model, the optimal data evaluation value expression for the stress value of the gear pump valve core specimen is determined as follows:

[0042]

[0043] Where w1 represents the residual weight coefficient, w2 represents the root mean square error weight coefficient, and w3 represents the determination coefficient weight coefficient. represents the average of the absolute values ​​of the residuals, RMSE represents the root mean square error, R 2 represents the coefficient of determination;

[0044] S8. By determining the optimal fitting method, the stress value of the gear pump valve core specimen of the same type is expanded at any time point, so as to predict the stress relaxation amount of the gear pump valve core specimen degradation model in advance.

[0045] Furthermore, in step S4, the collected stress value data of the gear pump valve core test piece are preprocessed, specifically comprising the following steps:

[0046] S41. Stress values ​​of the gear pump valve core specimen collected during the heating and cooling stages of the insulation box during the removal test;

[0047] S42, using a wavelet transform method to perform denoising on the collected stress values ​​of the gear pump valve core specimen;

[0048] S43, normalize the denoised stress value data to ensure consistency in subsequent processing. The expression is:

[0049] X= X i -X min

[0050] X max -X min

[0051] Where, X i represents the stress value data after denoising, X min Represents the minimum value of the data set, X max Indicates the maximum value of the data set.

[0052] Preferably, the multiple fitting methods in step S5 include a linear regression fitting method, a polynomial regression fitting method and a nonlinear regression fitting method.

[0053] The characteristics and beneficial effects of the present invention are:

[0054] 1. The gear pump valve core stress relaxation prediction method provided by this invention uses a logarithmic regression model of load loss rate and relaxation time to more accurately fit stress relaxation test data. A systematic error analysis and model validation strategy ensure that the model maintains high prediction accuracy under different experimental conditions. Compared with traditional linear models, this method performs better in capturing nonlinear relaxation behavior and significantly improves fitting accuracy.

[0055] 2. The gear pump valve core stress relaxation prediction method provided by the present invention has an integrated data processing and analysis process that reduces human errors and improves overall work efficiency. The automated data collection and calculation optimizes the laboratory workflow, allowing researchers to obtain effective results more quickly, and solves the problem that traditional methods mainly rely on large samples and long-term physical experiments, resulting in long testing time and low accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of a first data fitting method of the present invention;

[0057] Figure 2 This is a flow chart of the first method of starting a gear pump valve core test piece stress test of the present invention;

[0058] Figure 3 It is a flow chart for verifying the fitting quality of the regression model in the first data fitting method of the present invention;

[0059] Figure 4 It is a structural schematic diagram of the loading tooling of the present invention;

[0060] Figure 5 is the original data collected in Example 1 of the present invention;

[0061] Figure 6: is a logarithmic regression fitting curve of load loss rate and time in Example 1 of the present invention;

[0062] Figure 7 is the residual graph of embodiment 1 of the present invention;

[0063] Figure 8 Schematic diagram of the training process of embodiment 2 of the present invention;

[0064] Figure 9 2 is a schematic diagram showing the comparison between the training set prediction results and the real data in Example 2 of the present invention;

[0065] Figure 10 2 is a schematic diagram showing the comparison between the prediction results of the prediction set and the actual data in Example 2 of the present invention;

[0066] Figure 11 is a flow chart of a second data fitting method of the present invention;

[0067] Figure 12 is a schematic diagram of a second data fitting method of the present invention;

[0068] Figure 13 This is the fitting curve when the linear regression method is fitted using half the sample size in Example 3 of the present invention;

[0069] Figure 14 This is a residual graph when the linear regression method is fitted using half the sample size in Example 3 of the present invention;

[0070] Figure 15 This is the fitting curve when the linear regression method is used to fit the entire sample in Example 3 of the present invention;

[0071] Figure 16 This is the residual graph when the linear regression method is fitted using the entire sample in Example 3 of the present invention;

[0072] Figure 17 This is the fitting curve when the quadratic polynomial fitting method is performed using half the sample size in Example 3 of the present invention;

[0073] Figure 18 This is a residual graph when the quadratic polynomial fitting method is used to fit the half sample size in Example 3 of the present invention;

[0074] Figure 19 This is the fitting curve when the quadratic polynomial fitting method is used for all samples in Example 3 of the present invention;

[0075] Figure 20 This is a residual graph when the quadratic polynomial fitting method is used for fitting the entire sample in Example 3 of the present invention;

[0076] Figure 21This is the fitting curve when the cubic polynomial fitting method is performed using half the sample size in Example 3 of the present invention;

[0077] Figure 22 This is a residual graph when a cubic polynomial fitting method is used to fit a half-sample size in Example 3 of the present invention;

[0078] Figure 23 This is the fitting curve when the cubic polynomial fitting method is used for fitting the entire sample in Example 3 of the present invention;

[0079] Figure 24 This is a residual graph when the cubic polynomial fitting method is used to fit the entire sample in Example 3 of the present invention;

[0080] Figure 25 This is the fitting curve when the sine function model fitting method is performed using half the sample size in Example 3 of the present invention;

[0081] Figure 26 This is the fitting curve when the custom model fitting method is performed using half the sample size in Example 3 of the present invention.

[0082] Main reference numerals:

[0083] Fixed bracket 1; fixed end 2; loading end 3; loading push rod 4; protective cover 5; force sensor 6; connecting adapter 7; gear pump valve core test piece 8; data acquisition and processing module 9; loading push rod control module 10. DETAILED DESCRIPTION

[0084] To fully describe the technical content, structural features, objectives and effects of the present invention, the following is a detailed description with reference to the accompanying drawings.

[0085] A method for predicting stress relaxation of a gear pump valve core of the present invention is as follows: Figure 1 As shown, it includes the following steps:

[0086] S1. Select a plurality of identical gear pump valve core test pieces 8 to be tested and divide them into a first group of gear pump valve core test pieces, a second group of gear pump valve core test pieces, and an Nth group of gear pump valve core test pieces. In this embodiment, the Nth group of gear pump valve core test pieces is the third group of gear pump valve core test pieces.

[0087] S2, install the selected gear pump valve core specimen 8 on the loading fixture in the stress relaxation testing machine, start the loading fixture, and start the first group of gear pump valve core specimen 8 stress test, such as Figure 2 The specific steps are as follows:

[0088] S21. Install the first set of gear pump valve core test pieces on the loading fixture and set the initial test parameters;

[0089] S22, raising the temperature of the insulated box to a predetermined test temperature and performing heat preservation;

[0090] S23, starting the loading push rod to apply a loading force to the first group of gear pump valve core test pieces so that the gear pump valve core test pieces maintain a certain elastic displacement;

[0091] S24, setting the loading temperature and loading time of the gear pump valve core test piece;

[0092] S25. Recording the elastic force generated by the gear pump valve core test piece at each time point in real time using a force sensor;

[0093] S26. When the elastic force of the gear pump valve core specimen decays to the loading time, the stress test of the first group of gear pump valve core specimens is stopped.

[0094] S3, using the data acquisition and processing module 9 in the stress relaxation testing machine, collecting the stress values ​​of the gear pump valve core specimen 8 measured at each time point during the loading time in real time, storing the collected stress values ​​in the data acquisition and processing module 9, and dividing the data measured during the loading time into a training phase data set and a test phase data set, specifically comprising the following steps:

[0095] S31, performing equidistant sampling on the stress value data of the gear pump valve core test piece measured at each time point during the loading time, which is collected in real time;

[0096] S32, dividing the data after equal distance sampling into data sets;

[0097] S33, using 60% of the sampled data as a training phase data set, which can be used to fit the regression model of the gear pump valve core test piece;

[0098] S34. 40% of the sampled data is used as a test phase data set to test the error between the stress value predicted by the regression model and the actual stress value in the test phase.

[0099] S4. Calculate the load loss rate of the gear pump valve core specimen 8 at each time point during the training phase based on the training phase data set recorded in step S3. The expression is:

[0100]

[0101] Where, P0 represents the initial stress value of the gear pump valve core specimen, P t represents the stress value at time t;

[0102] S5. The least square method is used to fit the logarithmic regression model of the load loss rate and relaxation time of the gear pump valve core specimen 8. The expression is:

[0103]

[0104] Where, Indicates the load loss rate, t R represents the relaxation time, A and B represent the parameters of the regression model;

[0105] S6. Determine the parameter values ​​of A and B in the regression model of the first group of gear pump valve core test pieces 8 based on the regression model in step S5;

[0106] S7. Calculate the stress prediction value of the first group of gear pump valve core specimens 8 during the test phase based on the fitted regression model;

[0107] S8. Calculate the error between the stress prediction value of the first group of gear pump valve core specimens 8 during the test phase and the actual stress value recorded in step S3 during the test phase, and obtain the root mean square difference to verify the fitting quality of the regression model, such as Figure 3 As shown, the verification is carried out through the following steps:

[0108] S81. Calculate the root mean square error between the predicted value of the regression model and the actual stress value. The expression is:

[0109]

[0110] Where n represents the total number of samples. It represents the number of samples in the dataset, that is, the number of prediction-true value pairs we consider when calculating the error. i represents the actual value. In regression problems, this is the true observation value or label of the (i)th sample. f(x i ) represents the predicted value. This is the prediction result given by the model for the (i)th sample. Y i -f(x i ) represents the square of the error. It represents the square of the prediction error for the (i)th sample, reflecting the degree of deviation between the predicted value and the true value. The squaring operation amplifies large errors and ensures that the error is non-negative.

[0111] The smaller the RMSE, the closer the model's predicted value is to the actual value, and the better the model's predictive performance. RMSE is particularly suitable for comparing the performance of different models on the same or similar datasets.

[0112] S82. Calculate the coefficient of determination between the predicted value of the regression model and the actual stress value. The expression is:

[0113]

[0114] Where y i represents the actual stress value, represents the predicted value, represents the average value of the true observation value, and n represents the number of samples;

[0115] S83. Calculate the residual between the predicted value of the regression model and the actual stress value. The expression is:

[0116]

[0117] Where y i represents the actual stress value, represents the predicted value;

[0118] S84. Evaluate the data fitting quality of the gear pump valve core specimen based on the regression model through residual analysis;

[0119] S9. Repeat steps S2 to S8 to perform stress tests on the remaining gear pump valve core specimens, and select a set of gear pump valve core specimen regression models with the smallest root mean square difference as the optimal regression model;

[0120] S10. Through the optimal regression model, the stress value of the gear pump valve core specimen of the same type at any time point can be predicted, so that the stress relaxation amount of the gear pump valve core specimen can be predicted in advance.

[0121] In a preferred embodiment, the gear pump valve core test piece 8 includes a tensile gear pump valve core and a compression gear pump valve core.

[0122] In a preferred embodiment, the predetermined test temperature in step S22 is -35°C to 175°C.

[0123] In a preferred embodiment, the displacement control accuracy of the loading tool in step S23 is greater than or equal to ±0.1%.

[0124] In a preferred embodiment, the sampling frequency of the data acquisition and processing module in step S3 is greater than or equal to 1 Hz.

[0125] like Figure 4As shown, the loading fixture is primarily used to apply compressive force to a gear pump valve core test specimen 8, keeping it fixed at a certain displacement for extended periods of time, up to one month. The loading fixture comprises a fixed bracket 1, a fixed end 2, a loading end 3, and a loading push rod 4. The loading fixture is housed within a protective cover 5, which houses an insulated box. The fixed bracket 1 is equipped with a guide rail, and the first end of the fixed bracket 1 is provided with a fixed end 2. A force sensor 6 is mounted on the fixed end 2. This force sensor 6 measures the tensile force of the gear pump valve core, converting the tensile force into an electrical signal that is transmitted to a data acquisition and processing module 9. Ultimately, the tensile force value is obtained in the software for reliability measurement and evaluation. A connection adapter 7 is attached to the force sensor 6, which can be replaced depending on the product being tested. The loading push rod 4 is located at the second end of the fixed bracket 1. The loading push rod control module 10, controlled by a main control computer, transmits control signals through a drive circuit to control the loading and unloading linear motion of the loading push rod 4. The changing distance between the loading end 3 and the fixed end 2 produces a tensile effect on the product being tested, ultimately achieving the desired loading effect. The second end of the fixed bracket 1 is provided with a loading end 3, the loading end 3 is slidably arranged on the guide rail, and the first end of the loading push rod 4 passes through the side plate of the protective cover 5 and is connected to the loading end 3. The gear pump valve core specimen 8 is placed between the fixed end 2 and the loading end 3 and is located in the insulation box. When the loading push rod 4 is started, the loading push rod 4 drives the loading end 3 to move along the guide rail, which can apply a loading force to the gear pump valve core specimen 8.

[0126] Example 1

[0127] like Figures 5 to 7 As shown, the present invention provides a method for predicting stress relaxation of a gear pump valve core, and the specific steps are as follows:

[0128] S1. Select multiple identical gear pump valve core specimens 8 to be tested. The initial length of the selected gear pump valve core specimens to be tested is 10mm to 300mm; the gear pump valve core specimens can withstand a tensile / compression distance of ±100mm; the gear pump valve core specimens can withstand a maximum tension / pressure of 200N; the outer diameter of the gear pump valve core is 2mm to 10mm.

[0129] S2. Install the selected gear pump valve core specimen 8 onto the loading fixture in the stress relaxation testing machine, start the loading fixture, and begin stress testing of the first group of gear pump valve core specimens 8;

[0130] S3. Through the data acquisition and processing module 9 in the stress relaxation testing machine, the stress values ​​measured at each time point of the gear pump valve core specimen 8 during the loading time are collected in real time, and the collected stress values ​​are stored in the data acquisition and processing module 9, and the data measured during the loading time are divided into a training phase data set and a test phase data set, specifically: 3 groups of gear pump valve core pressure data of the same type are obtained and integrated into one data set, each group of data contains 360,000 time points; data sampling processing, equidistant sampling of 3 groups of 360,000 data each, a total of 3 groups of 10,000 data each; selection of training set and test set: select the first 60% of the data as training data, that is, the training phase data set, and the last 40% as test data, that is, the test phase data set.

[0131] S4. Calculate the load loss rate of the gear pump valve core specimen 8 at each time point during the training phase based on the training phase data set recorded in step S3. The expression is:

[0132]

[0133] Where, P0 represents the initial stress value of the gear pump valve core specimen, P t represents the stress value at time t;

[0134] S5. Use statistical software (MATLAB) to fit the data and adopt the least square method to fit the logarithmic regression model of the load loss rate and relaxation time of the gear pump valve core specimen. The expression is:

[0135]

[0136] Where, Indicates the load loss rate, t R represents the relaxation time, A and B represent the parameters of the regression model;

[0137] S6. Based on the regression model in step S5, determine the parameter values ​​of A and B in the regression model of the first group of gear pump valve core test pieces 8, and calculate A = -2.3675, B = 0.1094;

[0138] S7. Calculate the stress prediction value of the first group of gear pump valve core specimens during the test phase based on the fitted regression model;

[0139] S8. Calculate the error between the stress prediction value of the first group of gear pump valve core specimens during the test phase and the actual stress value recorded in step S3 during the test phase, and obtain the root mean square error (RMSE) and the coefficient of determination (R 2 ) as an evaluation indicator to verify the fitting quality of the regression model;

[0140] S9. Repeat steps S2 to S8 to perform stress tests on the remaining gear pump valve core specimens, that is, perform stress tests on the second group of gear pump valve core specimens and the third group of gear pump valve core specimens in sequence, and select the regression model of the group of gear pump valve core specimens with the smallest root mean square difference as the optimal regression model;

[0141] Table 1 Parameter values ​​of multiple groups of gear pump valve core test pieces

[0142] Serial number Parameter value A Parameter value B Root mean square error (RMSE) <![CDATA[Coefficient of determination (R 2 )]]> result 1 -2.3675 0.1094 0.8939 0.0101 reserve 2 -1.6154 0.1480 0.8010 0.0227 reserve 3 -4.8597 0.4868 12.8559 0.1506 give up

[0143] In model evaluation, it is usually desirable to have a smaller mean square error (MSE) as this indicates a better predictive performance of the model. Therefore, the fitting parameters for the gear pump valve core specimen were selected to be the most appropriate.

[0144] S10. Through the optimal regression model, the stress value of the gear pump valve core specimen of the same type at any time point can be predicted, so that the stress relaxation amount of the gear pump valve core specimen can be predicted in advance.

[0145] Furthermore, the present invention can also perform predictive analysis by constructing an LSTM model, specifically:

[0146] Step 1: Obtain 5 sets of gear pump valve core pressure data of the same type and integrate them into one data set, each set of data contains 360,000 time points.

[0147] Step 2: Data sampling: 5 groups of 360,000 data points are sampled at equal intervals, resulting in 5 groups of 10,000 data points each; training and test sets are selected: the first 60% of the data is used as training data, and the last 40% is used as test data;

[0148] Step 3: Data normalization: Normalize both the training and test data. By using the maximum and minimum values ​​of the variables, the original data is converted into data within a specific range, thereby eliminating the effects of dimension and order of magnitude.

[0149] Step 4: Build an LSTM: A Long Short-Term Memory (LSTM) network is a variant of the recurrent neural network (RNN) specifically designed for processing sequential data. It can handle sequence inputs of varying lengths and influences the current output by leveraging the hidden state from the previous time step. Traditional RNNs often suffer from vanishing or exploding gradients when learning long-term dependencies, making it difficult to effectively capture information spanning long time periods. LSTM addresses this issue by introducing three key gating mechanisms in its hidden layers: a forget gate, an input gate, and an output gate. These gating units regulate the flow of information and state updates, enabling LSTM to better retain and utilize long-term memory, resulting in excellent performance in tasks involving long-term dependencies.

[0150] Step 5: Build an LSTM: Long Short-Term Memory (LSTM) network. A variant of the recurrent neural network (RNN) is specifically designed for processing sequential data. It can handle sequence inputs of varying lengths and influences the current output by leveraging the hidden state from the previous time step. Traditional RNNs often suffer from vanishing or exploding gradients when learning long-term dependencies, making it difficult to effectively capture information spanning long time periods. LSTM addresses this issue by introducing three key gating mechanisms in its hidden layers: a forget gate, an input gate, and an output gate. These gating units regulate the flow of information and state updates, enabling LSTM to better retain and utilize long-term memory, resulting in excellent performance in tasks involving long-term dependencies.

[0151] f t =σ(W f [h t-1 ,x t ]+b f )

[0152] Where, f t Represents the output vector of the forget gate, with element values ​​ranging from 0 to 1. Each element represents the degree to which the corresponding component in the cell state is retained. A value of 0 indicates complete forgetting, and a value of 1 indicates complete retention; σ represents the Sigmoid activation function. It compresses the result of the linear combination to between 0 and 1; W f Represents the weight matrix of the forget gate. It contains the weights used to calculate the forget gate, which are connected with the current input and the hidden state of the previous moment; [h t-1 ,x t ] indicates that the expression represents the connection of two vectors; h t-1 represents the hidden state vector at the previous moment. It carries the information passed down from the previous time step; x t Represents the input vector of the current time step. It contains the external input data of the current time step; b f Represents the bias vector of the forget gate. It is used to adjust the output of each calculation and provide greater flexibility for the network.

[0153] Step 6: Construct an input gate to determine updates within the cell state: First, a Sigmoid activation function is used to generate an "input gate" vector, indicating which information can be updated. Simultaneously, a Tanh activation function is used to generate a "candidate state" vector, representing the new information content. These two vectors are multiplied together and added to the current cell state to determine the extent to which the new information is written.

[0154] I t =σ(W1[h t-1 ,x t ]+b1)

[0155]

[0156] Where, I t represents the input gate vector, whose element values ​​are between 0 and 1. It determines which elements of new information will be added to the cell state; σ represents the Sigmoid activation function. It compresses the result of the linear combination to between 0 and 1; W1 represents the weight matrix of the input gate. It is used to calculate the input gate vector, connecting the current input and the hidden state of the previous moment; [h t-1 ,x t ] represents the connection between the hidden state of the previous moment and the input vector of the current time step; h t-1 represents the hidden state at the previous moment; x t Represents the input vector of the current time step; b1 represents the bias vector of the input gate. It is used to adjust the output of the input gate vector; Represents the candidate cell state vector, and the result of the linear combination is compressed to between -1 and 1 through the tanh activation function. It represents the new information that may be added to the cell state at the current time step; tanh represents the hyperbolic tangent activation function. It is used to limit the value of the candidate state to between -1 and 1; W c represents the weight matrix used to calculate the candidate cell state; b c The bias vector representing the candidate cell state; C t Represents the updated cell state. It combines the output of the forget gate to represent the retained old information and the output of the input gate to represent the newly added information.

[0157] Step 7: Construct the output gate to determine the output at time t: Combine the current input and the hidden state at the previous moment to generate an output gate vector. This vector is then multiplied by the cell state processed by the Tanh activation function to obtain the hidden state output at the current moment.

[0158] O t =σ(W0[h t-1 ,x t ]+b0)

[0159] h t =O t tanhC t

[0160] Where, O t Represents the output gate vector, whose element values ​​are between 0 and 1. It determines which information in the cell state will be output to the current hidden state; σ represents the Sigmoid activation function. It is used to compress the result of the linear combination to between 0 and 1; W0 represents the weight matrix of the output gate. It is used to calculate the output gate vector, connecting the current input and the hidden state of the previous moment; [h t-1 ,xt ] represents the connection between the hidden state of the previous moment and the input vector of the current time step; h t-1 represents the hidden state at the previous moment; x t Represents the input vector of the current time step; b0 represents the bias vector of the output gate. It is used to adjust the calculation result of the output gate vector; h t Represents the hidden state of the current time step. It is obtained by multiplying the output gate vector with the nonlinear transformation of the cell state, that is, through the tanh function. It contains the output information of the current time step and will be passed to the next time step; C t Represents the cell state at the current time step. Contains information updated by the forget gate and input gate.

[0161] Step 8: Train and evaluate the Long Short-Term Memory (LSTM) model to obtain an optimized model: Train the LSTM model using the first dataset and then test it on the second dataset. During training, the root mean square error (RMSE) is used as the loss function to guide model optimization.

[0162]

[0163] Where n represents the total number of samples. It represents the number of samples in the dataset, that is, the number of prediction-true value pairs we consider when calculating the error. i represents the actual value. In regression problems, this is the true observation value or label of the (i)th sample. f(x i ) represents the predicted value. This is the prediction result given by the model for the (i)th sample. Y i -f(x i ) represents the square of the error. It represents the square of the prediction error for the (i)th sample, reflecting the degree of deviation between the predicted value and the true value. The squaring operation amplifies large errors and ensures that the error is non-negative.

[0164] The smaller the RMSE, the closer the model's predicted value is to the actual value, and the better the model's predictive performance. RMSE is particularly suitable for comparing the performance of different models on the same or similar datasets.

[0165] Example 2

[0166] In a preferred embodiment 2 of the present invention, a lifespan test was conducted on a gear pump valve core, simulating the lifespan prediction behavior of the gear pump valve core based on an LSTM model. The test was continued using a test piece that had already undergone durability testing. The test bench motor was energized, compressing the gear pump valve core to a certain displacement. A pressure sensor recorded the pressure value in real time under a high-temperature environment of 180°C. Six sets of pressure values ​​were collected and preprocessed. The six sets of test data were aligned and normalized to approximate 1. Using the operating status data, six sets of 10,000 length samples were obtained by equidistant sampling to obtain readable data over a complete time period. The operating status data was divided into a first data set and a second data set. 60% of the operating status data was used as the first data set for model training, and the remaining 40% was used as the second data set for lifespan prediction.

[0167] A long short-term memory (LSTM) model was constructed and trained using the operational status data. After model training, a second dataset was fed into the model for multiple iterations to predict the lifespan of the gear pump valve core.

[0168] In this example, the learning rate of the LSTM model was set to 0.0001 and 100 iterations of training were performed. By selecting the RMSE loss function, the model's prediction results were compared with the actual data to evaluate the accuracy of the prediction. The visualization results of the entire training process are shown in Figure 2. Figure 8 As shown in Figure 2, the trend of loss is shown. In addition, Figure 9 shows the comparison between the predicted results and the true values ​​on the training set, and Figure 10 The graphs show the comparison between the prediction results on the test set and the real data. These graphs help us intuitively understand the model performance and prediction accuracy, thus providing a basis for further adjustment and optimization of the model.

[0169] The gear pump valve core stress relaxation prediction method provided by the present invention can more accurately fit the stress relaxation test data by using a logarithmic regression model of load loss rate and relaxation time, and adopts a systematic error analysis and model verification strategy, so that the model can maintain a high prediction accuracy under different experimental conditions. Compared with the traditional linear model, this method performs better in capturing nonlinear relaxation behavior and significantly improves the fitting accuracy. In addition, the integrated data processing and analysis process reduces human errors and improves overall work efficiency. The automated data acquisition and calculation optimizes the laboratory workflow, allowing researchers to obtain effective results more quickly, solving the problem that traditional methods mainly rely on large samples, long-term physical experiments, and take a long time and are costly.

[0170] In a second aspect, the present invention provides a method for predicting stress relaxation of a gear pump valve core, such as Figure 11 and Figure 12 As shown, it includes the following steps:

[0171] S1. Select the gear pump valve core specimen to be tested;

[0172] S2. Install the selected gear pump valve core specimen on the loading fixture in the stress relaxation testing machine, start the loading fixture, and start the stress test of the gear pump valve core specimen;

[0173] S3, using the data acquisition and processing module in the stress relaxation testing machine to collect the initial stress value of the gear pump valve core test piece and the stress values ​​measured at each time point in real time, and store the collected stress values ​​in the data acquisition and processing module;

[0174] S4. Preprocessing the collected stress value data of the gear pump valve core test piece, specifically including the following steps:

[0175] S41. Stress values ​​of the gear pump valve core specimen collected during the heating and cooling stages of the insulation box during the removal test;

[0176] S42, using a wavelet transform method to perform denoising on the collected stress values ​​of the gear pump valve core specimen;

[0177] S43, normalize the denoised stress value data to ensure consistency in subsequent processing. The expression is:

[0178]

[0179] Where, X i represents the stress value data after denoising, X min Represents the minimum value of the data set, X max Indicates the maximum value of the data set.

[0180] S5. Using a variety of fitting methods, fit the stress value data pre-processed in step S4;

[0181] S6. Calculate the error between the predicted value in each fitting method and the actual stress value in step S3 to verify the fitting quality of each fitting method;

[0182] S7. Determine the optimal data fitting method for the stress value of the gear pump valve core specimen based on the preset fitting model. The fitting model expression is:

[0183]

[0184] Where w1 represents the residual weight coefficient, w2 represents the root mean square error weight coefficient, and w3 represents the determination coefficient weight coefficient. represents the average of the absolute values ​​of the residuals, RMSE represents the root mean square error, R 2 represents the coefficient of determination.

[0185] S8. Predicting the stress value of the same type of gear pump valve core specimen at any time point by using a determined fitting method, so as to predict the stress relaxation amount of the gear pump valve core specimen in advance.

[0186] Furthermore, the multiple fitting methods in step S5 include a linear regression fitting method, a polynomial regression fitting method, an exponential regression fitting method, and a nonlinear regression fitting method.

[0187] Example 3

[0188] like Figures 11 to 26 As shown, the gear pump valve core performance degradation model data expansion method provided by the present invention has the following specific steps:

[0189] S1. Select the gear pump valve core specimen to be tested;

[0190] S2. Install the selected gear pump valve core specimen onto the loading fixture in the stress relaxation testing machine, start the loading fixture, and begin the gear pump valve core specimen stress test, as follows:

[0191] S21. Fix the gear pump valve core specimen on a dedicated loading fixture and ensure that the compression of the gear pump valve core specimen is always maintained at 1.7 cm.

[0192] S22. Use a force sensor to record the stress relaxation data of the gear pump valve core specimen under this compression amount.

[0193] S23. Set the sampling frequency to 1 Hz and the test time to 100 hours, i.e. 360,000 seconds, to ensure data continuity and integrity during the entire gear pump valve core specimen relaxation process.

[0194] S3, using the data acquisition and processing module in the stress relaxation testing machine to collect the initial stress value of the gear pump valve core test piece and the stress values ​​measured at each time point in real time, and store the collected stress values ​​in the data acquisition and processing module;

[0195] S4. Preprocessing the collected stress value data of the gear pump valve core test piece, specifically including the following steps:

[0196] S41. After obtaining data for a total of 100 hours (360,000 seconds), there will be unstable data at the beginning and end of the loading fixture from the beginning to the end of loading. According to the time regulations for heating and cooling the test chamber, the collected data should be preliminarily checked and obvious errors or abnormal measurements should be removed to ensure the rationality of the data set. In this method, the first 1 / 10 and the last 1 / 100 of the data are removed from the sample data as a whole, that is, the data from 3600 seconds to 356,400 seconds is selected. The following two methods can be used for sample selection and estimation:

[0197] Method 1: Half-sample size estimation: Half-sample size estimation is performed by randomly sampling half of the original dataset. This method can be used to quickly capture data characteristics and is used for preliminary analysis and model validation. 50% of the data points are randomly sampled from the full dataset to ensure randomness and representativeness. This method is fast, computationally inefficient, and is suitable for preliminary exploration.

[0198] Method 2: Full Sample Estimation: Full sample estimation uses all collected data for comprehensive analysis to provide more accurate and reliable estimates. This method uses data from 3600-356400 seconds as the full sample estimate.

[0199] S42. The wavelet transform method is used to perform denoising on the stress values ​​of the gear pump valve core specimen collected, mainly for:

[0200] S421. Select a suitable mother wavelet (Daubechies wavelet) and perform multi-scale decomposition;

[0201] S422, performing threshold processing on the decomposed high-frequency coefficients to remove noise;

[0202] S423, reconstructing the signal to obtain denoised pressure data;

[0203] S43, normalize the denoised stress value data to ensure consistency in subsequent processing. The expression is:

[0204]

[0205] Where, X i represents the stress value data after denoising, X min Represents the minimum value of the data set, X max Indicates the maximum value of the data set.

[0206] S5. Using a variety of fitting methods to fit the stress value data preprocessed in step S4, the various fitting methods include linear regression fitting method, polynomial regression fitting method, exponential regression fitting method and nonlinear regression fitting method.

[0207] Method 1: Linear regression fitting method

[0208] Linear fitting is a basic statistical method used to describe the relationship between two variables. Its goal is to find a straight line that describes the linear relationship between the variables as accurately as possible. Linear fitting is typically performed using the least squares method. The goal of the least squares method is to find the line that minimizes the sum of the squares of the perpendicular distances from all data points to the fitted line.

[0209] y=ax+b

[0210] Where y represents the dependent variable (the response variable); x represents the independent variable (the predictor variable); a represents the slope, which represents the change in the dependent variable for each unit change in the independent variable; and b represents the intercept, which represents the value of the dependent variable when the independent variable is zero.

[0211] Specifically, when half sample size data is used, the total sample size is S = 359959, the sampling number is s = 176,380, and the fitting result is y = 0.0000036x-48.69. The fitted curve is as follows: Figure 13 As shown, the coefficient of determination is R 2 =0.031899, residual graph, such as Figure 14 When the full sample data is used, the total number of samples is S = 359959, the number of samples is s = 349159, and the result after fitting is y = 0.0000018x-48.53. The fitted curve is as follows Figure 15 As shown, the coefficient of determination is R 2 =0.029999, residual graph, such as Figure 16 shown.

[0212] Method 2: Quadratic polynomial fitting method

[0213] Quadratic polynomial fitting is a statistical method that approximates trends in a dataset using a quadratic polynomial function. Unlike linear fitting, quadratic polynomial fitting can better capture nonlinear relationships in the data. Quadratic polynomial fitting typically uses the least squares method to find the best-fit curve. The goal is to minimize the sum of the squares of the perpendicular distances from all data points to the fitted curve.

[0214] Specifically, when half sample size data is used, the total number of samples is S = 359959, the number of samples is s = 176,380, and the result after fitting is y = 0.0000000000042x 2 -0.0000041x-48.44, the fitted curve is as follows Figure 17 As shown, the coefficient of determination is R 2 =0.040893, residual graph, such as Figure 18When the full sample data is used, the total number of samples is S = 359959, the number of samples is s = 349159, and the result after fitting is

[0215] y=0.000000000042x 2 -0.0000041x-48.44, the fitted curve is as follows Figure 19 As shown, the coefficient of determination is R 2 =0.034731, residual graph, such as Figure 20 shown.

[0216] Method 3: Cubic polynomial fitting method

[0217] Cubic polynomial fitting is a statistical method that approximates the trend of a data set using a cubic polynomial. Compared to linear and quadratic polynomial fitting, cubic polynomial fitting can capture more complex nonlinear relationships and curvilinear variations in data. Cubic polynomial fitting typically uses the least squares method to find the best-fit curve, minimizing the sum of the squares of the perpendicular distances from the data points to the fitted curve.

[0218] Specifically, when half sample size data is used, the total sample size is S = 359959, the number of samples is s = 176,380, and the result after fitting is y = -0.00000000000000007x 3 +0.00000000024x 2 -0.000019x-48.2, the fitted curve is as follows Figure 21 As shown, the coefficient of determination is R 2 =0.045997, residual graph, such as Figure 22 When the full sample data is used, the total number of samples is S = 359959, the number of samples is s = 349159, and the result after fitting is y = -0.000000000000000005651x 3 +0.000000000022x 2 +0.00000029x-48.56, the fitted curve is as follows Figure 23 As shown, the coefficient of determination is R 2 =0.036602, residual graph, such as Figure 24 shown.

[0219] Method 4: Sine function model fitting method

[0220] Sine function fitting is a technique used to fit data to a sinusoidal form. It is often used to analyze data with periodic or fluctuating characteristics. Its goal is to find appropriate sine function parameters so that the function can best describe the periodic characteristics of the data. A typical sine function can be expressed as:

[0221] y=A / sin(Bx+C)+D

[0222] Where A is the amplitude, which represents the height of the wave; B is the angular frequency; C is the phase offset, which controls the horizontal movement of the waveform on the time axis; and D is the vertical offset, which represents the overall movement of the waveform in the vertical direction.

[0223] Specifically, when half sample size data is used, the total sample size is S = 359959, the sampling number is s = 176,380, and the fitted curve is as follows: Figure 25 As shown, the coefficient of determination is R 2 =0.03849.

[0224] Method 5: Custom model fitting

[0225] Custom model fitting, its expression is:

[0226] f(x)=a*(sin(x-pi))+b*((x-10) 2 )+c

[0227] Where a represents the amplitude of the sine function; b represents the coefficient of the quadratic term; and c represents the coefficient of the quadratic term.

[0228] Specifically, when half sample size data is used, the total number of samples is S = 359959, the number of samples is s = 176,380, and the result after fitting is y = a*(sin(x-pi))+b*((x-10) 3 )+c*x 2 +d*x+e, where a=0.0004233, b=-7.036e-16, c=2.355e-10, d=-1.858e-05, e=-48.2. The fitted curve is as follows Figure 26 As shown, the coefficient of determination is R 2 =0.046.

[0229] S6. Calculate the error between the predicted value in each fitting method and the actual stress value in step S3 to verify the fitting quality of each fitting method. Specifically, calculate the coefficient of determination between the predicted value of the regression model and the actual stress value. The expression is:

[0230]

[0231] Where y i represents the actual stress value, represents the predicted value, represents the average value of the true observation value, and n represents the number of samples;

[0232] S7. Based on the preset fitting model, the optimal data evaluation value expression for the stress value of the gear pump valve core specimen is determined as follows:

[0233]

[0234] Where w1 represents the residual weight coefficient, w2 represents the root mean square error weight coefficient, and w3 represents the determination coefficient weight coefficient. Represents the average of the absolute values ​​of the residuals, and RMSE stands for root mean square error. It provides an overall measure of the prediction error, emphasizing large errors because it squares the error. A smaller RMSE indicates a good overall prediction performance of the model. 2 R represents the coefficient of determination, which measures the model's ability to explain data changes. The closer it is to 1, the stronger the model's ability to explain the data. By multiplying it by the weight c, we can flexibly adjust R according to specific needs. 2 The comprehensive evaluation formula can effectively integrate the residual value, RMSE and R 2 The information provided by the model provides valuable guidance for model selection and optimization. In this embodiment, w1=3, w2=4, and w3=3 are selected for evaluation.

[0235] S8. Predicting the stress value of the same type of gear pump valve core specimen at any time point by using a determined fitting method, so as to predict the stress relaxation amount of the gear pump valve core specimen in advance.

[0236] The present invention integrates multiple data fitting methods to select the most suitable fitting model according to different data characteristics. In this way, the changing trends of each stage in the gear pump valve core relaxation process can be captured more accurately, thereby improving the accuracy of data analysis. At the same time, a multi-model fitting strategy is adopted, so that the system can adapt to gear pump valve core relaxation test data of different types and materials. Whether it is linear, nonlinear or exponential changes, the present invention can provide a suitable fitting solution to increase the flexibility and scope of application of data processing. In addition, the ability to automatically evaluate and select the best fitting model makes the entire data processing process more efficient. The process of manual model selection is reduced, the possibility of human error is reduced, and overall work efficiency is improved. Through optimized algorithm design and preprocessing steps, the present invention can quickly process large amounts of data, significantly reduce data processing time, and provide technical support for the research on performance degradation models of key components of gear pumps and data expansion methods.

[0237] The above embodiments are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for predicting stress relaxation of a gear pump valve core, characterized in that: It includes the following steps: S1. Select a plurality of identical gear pump valve core test pieces to be tested, and divide them into a first group of gear pump valve core test pieces, a second group of gear pump valve core test pieces, and an Nth group of gear pump valve core test pieces; S2. Install the selected gear pump valve core specimens onto a loading fixture in the stress relaxation testing machine, start the loading fixture, and begin stress testing of the first group of gear pump valve core specimens; S3, using a data acquisition and processing module in the stress relaxation testing machine to collect in real time the stress values ​​of the gear pump valve core specimen measured at various time points during the loading time, and dividing the data measured during the loading time into a training phase data set and a test phase data set; S4. Calculate the load loss rate of the gear pump valve core specimen at each time point during the training phase based on the training phase data set recorded in step S3. The expression is: Where, P0 represents the initial stress value of the gear pump valve core specimen, P t represents the stress value at time t; S5. The least square method is used to fit the logarithmic regression model of the load loss rate and relaxation time of the gear pump valve core specimen, and the expression is: Where, Indicates the load loss rate, t R represents the relaxation time, A and B represent the parameters of the regression model; S6. Determine the parameter values ​​of A and B in the regression model of the first group of gear pump valve core specimens based on the regression model in step S5; S7. Calculate the stress prediction value of the first group of gear pump valve core specimens during the test phase based on the fitted regression model; S8. Calculate the error between the predicted stress value of the first group of gear pump valve core specimens during the test phase and the actual stress value recorded in step S3 during the test phase, and obtain the root mean square difference to verify the fitting quality of the regression model; S9, repeating steps S2 to S8, performing stress tests on the second group of gear pump valve core specimens and the Nth group of gear pump valve core specimens in sequence, and selecting the regression model of the group of gear pump valve core specimens with the smallest root mean square difference as the optimal regression model; S10. Through the optimal regression model, the stress value of the gear pump valve core specimen of the same type at any time point can be predicted, thereby predicting the stress relaxation amount of the gear pump valve core specimen.

2. The method for predicting stress relaxation of a gear pump valve core according to claim 1, wherein: In step S2, the selected gear pump valve core specimen is mounted on a loading fixture in a stress relaxation testing machine, the loading fixture is started, and the stress test of the first group of gear pump valve core specimens is started. The specific steps include: S21. Install the first set of gear pump valve core test pieces on the loading fixture and set the initial test parameters; S22, raising the temperature of the insulated box to a predetermined test temperature and performing heat preservation; S23, starting the loading push rod to apply a loading force to the first group of gear pump valve core test pieces so that the gear pump valve core test pieces maintain a certain elastic displacement; S24, setting the loading temperature and loading time of the gear pump valve core test piece; S25. Recording the elastic force generated by the gear pump valve core test piece at each time point in real time using a force sensor; S26. When the elastic force of the gear pump valve core specimen decays to the loading time, the stress test of the first group of gear pump valve core specimens is stopped.

3. The method for predicting stress relaxation of a gear pump valve core according to claim 2, wherein: The displacement control accuracy of the loading fixture in step S23 is greater than or equal to ±0.1%.

4. The method for predicting stress relaxation of a gear pump valve core according to claim 1, wherein: Step S3 divides the data measured during the loading time into a training phase data set and a testing phase data set, and specifically includes the following steps: S31, performing equidistant sampling on the stress value data of the gear pump valve core test piece measured at each time point during the loading time, which is collected in real time; S32, dividing the data after equal distance sampling into data sets; S33, using 60% of the sampled data as a training phase data set, which can be used to fit the regression model of the gear pump valve core test piece; S34. 40% of the sampled data is used as a test phase data set to test the error between the stress value predicted by the regression model and the actual stress value in the test phase.

5. The method for predicting stress relaxation of a gear pump valve core according to claim 4, wherein: The sampling frequency of the data acquisition and processing module in step S3 is greater than or equal to 1 Hz.

6. A method for expanding data of a gear pump valve core performance degradation model, characterized in that: It includes the following steps: S1. Select the gear pump valve core specimen to be tested; S2. Install the selected gear pump valve core specimen on the loading fixture in the stress relaxation testing machine, start the loading fixture, and start the stress test of the gear pump valve core specimen; S3, using a data acquisition and processing module in the stress relaxation testing machine to collect the initial stress value of the gear pump valve core specimen and the stress values ​​measured at each time point in real time, and storing the collected stress values ​​in the data acquisition and processing module; S4, preprocessing the collected stress value data of the gear pump valve core test piece; S5. Using a variety of fitting methods, fit the stress value data pre-processed in step S4; S6. Calculate the error between the predicted value in each fitting method and the actual stress value in step S3 to verify the fitting quality of each fitting method; S7. Based on the preset fitting model, the optimal data evaluation value expression for the stress value of the gear pump valve core specimen is determined as follows: Where w1 represents the residual weight coefficient, w2 represents the root mean square error weight coefficient, and w3 represents the determination coefficient weight coefficient. represents the average of the absolute values ​​of the residuals, RMSE represents the root mean square error, R 2 represents the coefficient of determination; S8. By determining the optimal fitting method, the stress value of the similar gear pump valve core specimen at any time point is expanded, thereby predicting the stress relaxation amount of the gear pump valve core degradation model.

7. The gear pump valve core performance degradation model data expansion method according to claim 6, characterized in that: In step S4, the collected stress value data of the gear pump valve core test piece are preprocessed, which specifically includes the following steps: S41. Stress values ​​of the gear pump valve core specimen collected during the heating and cooling stages of the insulation box during the removal test; S42, using a wavelet transform method to perform denoising on the collected stress values ​​of the gear pump valve core specimen; S43, normalize the denoised stress value data to ensure consistency in subsequent processing. The expression is: Where, X i represents the stress value data after denoising, X min Represents the minimum value of the data set, X max Indicates the maximum value of the data set.

8. The method for expanding data of a gear pump valve core performance degradation model according to claim 6, characterized in that: The multiple fitting methods in step S5 include a linear regression fitting method, a polynomial regression fitting method, and a nonlinear regression fitting method.

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