Life prediction method based on accelerated life test

Abstract

The invention discloses a service life prediction method based on an accelerated service life test, relates to the technical field of rubber service life prediction, and solves the technical problem that the accuracy of subway rubber service life prediction is not high due to the fact that test conditions of a traditional rubber aging test are difficult to accurately control. The method comprises the following steps: performing an accelerated life test on to-be-tested subway rubber; performing curve fitting based on the plurality of groups of test data to obtain a plurality of performance fitting functions; performing iterative optimization on each performance fitting function based on the decision coefficient and the mean square error; and analyzing the service life of the to-be-tested subway rubber based on the plurality of performance fitting functions to obtain the residual service life. The method comprises the following steps: fitting test data of to-be-tested subway rubber through a quadratic function to obtain a performance fitting function; and the performance fitting function is iteratively optimized according to the decision coefficient and the mean square deviation, so that the attenuation trend of the subway rubber performance can be accurately represented, and the accuracy of predicting the service life of the subway rubber can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of rubber life prediction, and in particular is a life prediction method based on accelerated life test. Background Art

[0002] As the operation time of subway vehicles increases, rubber parts tend to deteriorate and age, which affects their performance and life. Therefore, exploring the relationship between various performance parameters of rubber and its remaining life and accurately predicting the service life of subway vehicle rubber parts are of great significance to ensure the safe and economical operation of the subway system.

[0003] Although the traditional natural aging test of rubber can simulate the real degradation process of rubber, the test cycle is relatively long. Therefore, in order to simulate the long-term use process that rubber experiences in a relatively short period of time, so as to obtain the changing trend of rubber performance parameters and life prediction information more quickly, accelerated life tests are carried out on rubber.

[0004] However, the rubber aging method of the prior art has the following problems:

[0005] 1) Long test cycle: Traditional natural aging tests rely on natural environmental conditions, such as temperature, humidity, and light, which change slowly, so the test cycle is often long. This means that researchers need to wait a long time to get test results, which is inefficient for research and product development.

[0006] 2) Test conditions are difficult to control: Temperature, humidity, light and other conditions in the natural environment are constantly changing and difficult to control accurately. This may lead to inconsistency in test results, because different environmental conditions may lead to different aging rates and degrees, thus affecting the accuracy and reliability of the test.

[0007] 3) Poor repeatability of test results: Due to the unpredictability and variability of natural environmental conditions, the results of each test may be different. This makes it difficult to analyze and interpret the data, especially in scientific research or product quality control, where repeatability is a key factor in ensuring the validity and reliability of the results.

[0008] The present invention proposes a life prediction method based on accelerated life test to solve the above technical problems. Summary of the invention

[0009] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a life prediction method based on accelerated life test, which is used to solve the problem that traditional rubber aging test depends on natural environmental conditions, and the natural environmental conditions change slowly, resulting in a long test cycle, making the prediction of subway rubber life inefficient; in addition, the test conditions of traditional rubber aging test are difficult to accurately control, resulting in low accuracy of subway rubber life prediction.

[0010] To achieve the above object, a first aspect of the present invention provides a life prediction method based on an accelerated life test, comprising:

[0011] S1: Perform accelerated life test on the subway rubber to be tested; the subway rubber to be tested includes door finger rubber and gangway rubber; the door finger rubber includes new door finger rubber and door finger rubber that has been in service for 5 years; the gangway rubber includes new gangway rubber and gangway rubber that has been in service for 5 years;

[0012] S2: obtaining several groups of test data corresponding to the performance parameters of the subway rubber to be tested in the accelerated life test; wherein the performance parameters include the elongation at break, tensile strength and hardness of the door finger rubber and the hardness of the gangway rubber;

[0013] S3: performing curve fitting based on several groups of test data to obtain several performance fitting functions;

[0014] S4: Iteratively optimize each performance fitting function based on the coefficient of determination and mean square error;

[0015] S5: Analyze the service life of the subway rubber to be tested based on several performance fitting functions to obtain the remaining service life.

[0016] Preferably, the accelerated life test of the subway rubber to be tested comprises:

[0017] The subway rubber to be tested is placed in an aging box at a preset temperature and stored for several days to perform an accelerated life test.

[0018] Preferably, the step of obtaining several groups of test data corresponding to the performance parameters of the subway rubber to be tested in the accelerated life test includes:

[0019] The performance parameters of the door finger rubber and the hardness of the tunnel rubber are obtained; the performance parameters of the door finger rubber and the hardness of the tunnel rubber are divided into several groups of test data, and each group of test data is marked as i; wherein i=1, 2, ..., n, and n is the total number of groups of test data.

[0020] Preferably, the curve fitting based on several groups of test data comprises:

[0021] A1: Extract several groups of test data corresponding to the elongation at break, tensile strength, and hardness of the door finger guard rubber in the test data, as well as several groups of test data corresponding to the hardness of the gangway rubber.

[0022] A2: Perform curve fitting on the elongation at break, tensile strength, and hardness of the door finger guard rubber respectively through quadratic functions to obtain several performance fitting functions.

[0023] A3: Perform curve fitting on the hardness of the gangway rubber through quadratic functions to obtain several performance fitting functions.

[0024] Preferably, the iterative optimization of each performance fitting function based on the coefficient of determination and mean square error includes:

[0025] B1: Evaluate the fitting effect of the performance fitting function based on the coefficient of determination and mean square error to obtain an evaluation result; among them, the evaluation result includes poor fitting effect and good fitting effect.

[0026] B2: Iteratively optimize the performance fitting function based on the evaluation result.

[0027] Preferably, the evaluation of the fitting effect of the performance fitting function based on the coefficient of determination and mean square error includes:

[0028] C1: Extract several performance fitting functions corresponding to the performance parameters.

[0029] C2: Through the formula Calculate the coefficient of determination R 2 ; where, y i is the i-th group of test data, is the predicted value of the fitting curve corresponding to the i-th group of test data, y mean is the average value of the test data;

[0030] C3: Through the formula Calculate the mean square error MSE;

[0031] C4: Set the coefficient of determination threshold as YZ1 and the mean square error threshold as YZ2; where, the value range of YZ1 is 0 < YZ1 < 1, and the value range of YZ2 is 0 < YZ2 < 1;

[0032] C5: Judge whether the coefficient of determination R 2 > YZ1 and the mean square error MSE < YZ2 holds; if yes, mark the evaluation result as good fitting effect; if not, mark the evaluation result as poor fitting effect.

[0033] It should be noted that the closer the coefficient of determination R2 is to 1 and the closer the mean square error MSE is to 0, the better the fitting effect of the performance fitting function.

[0034] Preferably, the iterative optimization of the performance fitting function based on the evaluation result includes:

[0035] D1: Extract the evaluation results corresponding to each performance fitting function;

[0036] D2: Determine whether the evaluation result is poor fitting effect; if yes, re-curve fit the corresponding performance fitting function and jump to D1; if no, retain the performance fitting function.

[0037] The invention fits the performance parameters of the subway rubber to be tested that change with time through a quadratic function to obtain a performance fitting function; evaluates the fitting effect of the performance fitting function according to the determination coefficient and the mean square deviation to obtain an evaluation result; and refits the performance fitting function whose evaluation result is that the fitting effect is poor, so that the performance fitting function can accurately and quickly express the trend of the performance attenuation of the subway rubber with time, thereby facilitating improving the accuracy and efficiency of predicting the service life of the subway rubber.

[0038] Preferably, the life of the subway rubber to be tested is analyzed based on several performance fitting functions, including:

[0039] E1: Obtain the initial performance value and current performance value of the subway rubber performance parameter to be tested;

[0040] E2: Set the performance failure critical value SXJ corresponding to each performance parameter; the performance failure critical value is the ratio between the performance value of the subway rubber to be tested when it fails and the initial performance value;

[0041] E3: By formula Calculate the failure assessment coefficient SPX; where XN0 is the initial performance value and XN is the current performance value;

[0042] E4: Let the failure assessment coefficient be equal to the corresponding performance failure critical value, solve to obtain the performance value when the performance parameter of the subway rubber to be tested fails, and mark the performance value at the time of failure as the failure value; substitute the failure value into the corresponding performance fitting function to calculate the corresponding failure age;

[0043] E5: Determine whether the failure evaluation coefficient of each performance parameter is less than the corresponding performance failure critical value; if yes, input the current value of the performance parameter into the corresponding performance fitting function to calculate the corresponding usage time, and jump to E6; if no, mark the life of the corresponding subway rubber to be tested as failure;

[0044] E6: Subtract the usage time from the corresponding failure year to get the remaining life, and mark the minimum value of the remaining life corresponding to each performance parameter as the remaining service life.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The invention fits the performance parameters of the subway rubber to be tested that change with time through a quadratic function to obtain a performance fitting function; evaluates the fitting effect of the performance fitting function according to the determination coefficient and the mean square deviation to obtain an evaluation result; and refits the performance fitting function whose evaluation result is that the fitting effect is poor, so that the performance fitting function can accurately and quickly express the trend of the performance attenuation of the subway rubber with time, thereby facilitating improving the accuracy and efficiency of predicting the service life of the subway rubber. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is an overall flow chart of the life prediction method of the present invention;

[0049] Figure 2 Schematic diagram of curve fitting of the elongation at break of the door finger guard rubber in this embodiment;

[0050] Figure 3 Schematic diagram of curve fitting of the tensile strength of the door finger guard rubber in this embodiment;

[0051] Figure 4 Schematic diagram of curve fitting of the hardness of the door finger guard rubber in this embodiment;

[0052] Figure 5 Schematic diagram of curve fitting of the fracture hardness of the through-channel rubber in this embodiment. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1-Figure 5 The first embodiment of the present invention provides a life prediction method based on accelerated life test, comprising:

[0055] S1: Perform accelerated life test on the subway rubber to be tested; the subway rubber to be tested includes door finger rubber and gangway rubber; the door finger rubber includes new door finger rubber and door finger rubber that has been in service for 5 years; the gangway rubber includes new gangway rubber and gangway rubber that has been in service for 5 years;

[0056] S2: obtaining several groups of test data corresponding to the performance parameters of the subway rubber to be tested in the accelerated life test; wherein the performance parameters include the elongation at break, tensile strength and hardness of the door finger rubber and the hardness of the gangway rubber;

[0057] S3: performing curve fitting based on several groups of test data to obtain several performance fitting functions;

[0058] S4: Iteratively optimize each performance fitting function based on the coefficient of determination and mean square error;

[0059] S5: Analyze the service life of the subway rubber to be tested based on several performance fitting functions to obtain the remaining service life.

[0060] In this embodiment, an accelerated life test is performed on the subway rubber to be tested, including:

[0061] The subway rubber to be tested is placed at a preset temperature of 50°C and stored for 36 days for an accelerated life test.

[0062] In this embodiment, several groups of test data corresponding to the performance parameters of the subway rubber to be tested in the accelerated life test are obtained, including:

[0063] The performance parameters of the door finger rubber and the hardness of the tunnel rubber are obtained; the performance parameters of the door finger rubber and the hardness of the tunnel rubber are divided into several groups of test data, and each group of test data is marked as i; wherein i=1, 2, ..., n, and n is the total number of groups of test data.

[0064] Exemplarily, 4 groups of test data of the subway rubber to be tested in the accelerated life test are obtained.

[0065] In this embodiment, curve fitting is performed based on several groups of test data, including:

[0066] A1: Extract several groups of test data corresponding to the elongation at break, tensile strength and hardness of the door finger rubber, and several groups of test data corresponding to the hardness of the tunnel rubber;

[0067] A2: The elongation at break, tensile strength and hardness of the door finger rubber are respectively fitted by quadratic functions to obtain several performance fitting functions;

[0068] A3: The hardness of the through-channel rubber is curve-fitted using a quadratic function to obtain several performance fitting functions.

[0069] For example, Figure 2-5 As shown, the performance fitting function of the elongation at break of the new door finger rubber is u1 = 0.61x 2 -9.98x+245.86;

[0070] The performance fitting function of the elongation at break of the door finger rubber that has been in service for 5 years is y2 = -0.27x 2 -8.39x+298.01;

[0071] The performance fitting function of the tensile strength of the new door finger rubber is y3 = 0.0094x 2 -0.16x+9.78;

[0072] The performance fitting function of the tensile strength of the door finger rubber that has been in service for 5 years is y4 = -0.00135x 2 -0.078x+8.43;

[0073] The performance fitting function of the elongation at break of the new door finger rubber is y5 = 0.036x 2 +0.129x+63.01;

[0074] The performance fitting function of the elongation at break of the door finger rubber that has been in service for 5 years is y6 = -0.0027x 2 +0.52x+70.09;

[0075] The performance fitting function corresponding to the hardness of the new tunnel rubber is y7 = -0.109x 2 +1.62x+44.81;

[0076] The performance fitting function corresponding to the hardness of the tunnel rubber that has been in service for 5 years is y8 = -0.023x 2 +1.14x+53.198.

[0077] In this embodiment, each performance fitting function is iteratively optimized based on the determination coefficient and the mean square error, including:

[0078] B1: Evaluate the fitting effect of the performance fitting function based on the determination coefficient and the mean square error to obtain the evaluation results; wherein the evaluation results include poor fitting effect and good fitting effect;

[0079] B2: Iteratively optimize the performance fitting function based on the evaluation results.

[0080] In this embodiment, the fitting effect of the performance fitting function is evaluated based on the determination coefficient and the mean square error, including:

[0081] C1: Extract several performance fitting functions corresponding to the performance parameters;

[0082] C2: Calculate the coefficient of determination R through the formula; where, y 2 is the i-th group of test data, i is the predicted value of the fitting curve corresponding to the i-th group of test data, and y is the average value of the test data; mean

[0083] C3: Calculate the mean square error MSE through the formula ;

[0084] C4: Set the coefficient of determination threshold to YZ1 and the mean square error threshold to YZ2; where, the value range of YZ1 is 0 < YZ1 < 1, and the value range of YZ2 is 0 < YZ2 < 1;

[0085] C5: Determine whether the coefficient of determination R 2 > YZ1 and the mean square error MSE < YZ2 holds; if yes, mark the evaluation result as good fitting effect; if not, mark the evaluation result as poor fitting effect.

[0086] It should be noted that the closer the coefficient of determination R2 is to 1 and the closer the mean square error MSE is to 0, the better the fitting effect of the performance fitting function.

[0087] Exemplarily, set the coefficient of determination threshold to YZ1 = 0.96 and the mean square error threshold to YZ2 = 0.5;

[0088] The coefficient of determination, mean square error, and evaluation results of several performance fitting functions corresponding to the subway rubber performance parameters to be measured are shown in the following table:

[0089]

[0090]

[0091] As can be seen from the above table, since the evaluation results of each performance fitting function are good fitting effects, that is, the performance fitting functions in this embodiment can accurately fit the trend of the subway rubber performance changing with time.

[0092] In this embodiment, based on the evaluation results, iterative optimization of the performance fitting function is performed, including:

[0093] D1: Extract the evaluation results corresponding to each performance fitting function;

[0094] D2: Determine whether the evaluation result is a poor fitting effect; if yes, re-perform curve fitting on the corresponding performance fitting function, and jump to D1; if not, retain the performance fitting function.

[0095] The invention fits the performance parameters of the subway rubber to be tested that change with time through a quadratic function to obtain a performance fitting function; evaluates the fitting effect of the performance fitting function according to the determination coefficient and the mean square deviation to obtain an evaluation result; and refits the performance fitting function whose evaluation result is that the fitting effect is poor, so that the performance fitting function can accurately and quickly express the trend of the performance attenuation of the subway rubber with time, thereby facilitating improving the accuracy and efficiency of predicting the service life of the subway rubber.

[0096] In this embodiment, the life of the subway rubber to be tested is analyzed based on several performance fitting functions, including:

[0097] E1: Obtain the initial performance value and current performance value of the subway rubber performance parameter to be tested;

[0098] E2: Set the performance failure critical value SXJ corresponding to each performance parameter; the performance failure critical value is the ratio between the performance value of the subway rubber to be tested when it fails and the initial performance value;

[0099] E3: By formula Calculate the failure assessment coefficient SPX; where XN0 is the initial performance value and XN is the current performance value;

[0100] E4: Let the failure assessment coefficient be equal to the corresponding performance failure critical value, solve to obtain the performance value when the performance parameter of the subway rubber to be tested fails, and mark the performance value at the time of failure as the failure value; substitute the failure value into the corresponding performance fitting function to calculate the corresponding failure age;

[0101] E5: Determine whether the failure evaluation coefficient of each performance parameter is less than the corresponding performance failure critical value; if yes, input the current value of the performance parameter into the corresponding performance fitting function to calculate the corresponding usage time, and jump to E6; if no, mark the life of the corresponding subway rubber to be tested as failure;

[0102] E6: Subtract the usage time from the corresponding failure year to get the remaining life, and mark the minimum value of the remaining life corresponding to each performance parameter as the remaining service life.

[0103] It should be noted that when there are multiple performance parameters of subway rubber, such as the performance parameters of door finger guard rubber include elongation at break, tensile strength and hardness, and there are multiple remaining lifespans obtained by the performance fitting function, the minimum value of the remaining lifespans corresponding to each performance parameter shall be marked as the remaining service life; when there is only one performance parameter of subway rubber, the remaining lifespan obtained by the performance fitting function shall be directly marked as the remaining service life.

[0104] Exemplarily, the performance failure critical value SXJ1 of the elongation at break of the door finger rubber is set to be 50%, the performance failure critical value SXJ2 of the tensile strength of the door finger rubber is set to be 7%, and the performance failure critical value SXJ3 of the hardness of the door finger rubber is set to be 10%;

[0105] The initial performance values ​​of the door finger rubber are set to be elongation at break DSL1=246.86%, tensile strength LSQ1=9.76MPa, and hardness YD1=70.3HA;

[0106] Set the current performance values ​​of the door finger guard rubber to elongation at break DSL2=211.83%, tensile strength LSQ2=9.10MPa, and hardness YD2=73.4HA;

[0107] The failure evaluation coefficient of the elongation at break of the door finger rubber SXP1≈14.19%, the failure evaluation coefficient of the tensile strength of the door finger rubber SXP2≈6.86%, and the failure evaluation coefficient of the hardness of the door finger rubber SXP3≈4.41% were calculated by the formula.

[0108] Let the failure evaluation coefficient be equal to the corresponding performance failure critical value, and solve to obtain the failure value of the elongation at break of the door finger rubber SXZ1 = 123.43%, the failure value of the tensile strength of the door finger rubber SXZ2 = 9.0768 MPa, and the failure value of the hardness of the door finger rubber SXZ3 = 77.33 HA;

[0109] Substituting the failure values ​​SXZ1, SXZ2, and SXZ3 into the corresponding performance fitting functions y1, y3, and y5 for calculation, the failure age of the elongation at break of the door finger rubber SXN1≈16.33 years, the failure age of the tensile strength of the door finger rubber SXN2≈15.05 years, and the failure age of the hardness of the door finger rubber SXN3≈18.42 years are obtained;

[0110] Since the failure evaluation coefficients SXP1, SXP2 and SXP3 are all less than the corresponding performance failure critical values, DSL2 = 211.83%, LSQ2 = 9.10 MPa, and YD2 = 73.4 HA are respectively substituted into the corresponding performance fitting functions, and the use time of the elongation at break of the door finger rubber sc1≈11.52 years, the use time of the tensile strength of the door finger rubber sc2≈8.81 years, and the use time of the hardness of the door finger rubber sc3≈15.29 years are obtained;

[0111] Subtract the usage time from the corresponding failure years to get the remaining life of the door finger rubber elongation at break sy1≈4.81 years, the remaining life of the door finger rubber tensile strength sy2≈6.24 years, and the remaining life of the door finger rubber hardness sy3≈3.13 years.

[0112] Take the minimum value sy3≈3.13 years among sy1, sy3, and sy5, and mark it as the remaining service life of the door finger guard rubber.

[0113] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A life prediction method based on accelerated life test, characterized in that: Including: S1: Conduct an accelerated life test on the subway rubber to be tested; among them, the subway rubber to be tested includes door finger guard rubber and through-channel rubber; the door finger guard rubber includes brand-new door finger guard rubber and door finger guard rubber that has been in service for 5 years; the through-channel rubber includes brand-new through-channel rubber and through-channel rubber that has been in service for 5 years; S2: Obtain several groups of test data corresponding to the performance parameters of the subway rubber to be tested in the accelerated life test; among them, the performance parameters include the elongation at break, tensile strength, and hardness of the door finger guard rubber, and the hardness of the through-channel rubber; S3: Perform curve fitting based on several groups of test data to obtain several performance fitting functions; S4: Iteratively optimize each performance fitting function based on the coefficient of determination and mean square error; S5: Analyze the life of the subway rubber to be tested based on several performance fitting functions to obtain the remaining service life.

2. The life prediction method based on accelerated life test according to claim 1, characterized in that: The conducting of the accelerated life test on the subway rubber to be tested includes: Place the subway rubber to be tested in an aging chamber at a preset temperature and store it for several days to conduct the accelerated life test.

3. The life prediction method based on accelerated life test according to claim 1, characterized in that: The obtaining of several groups of test data corresponding to the performance parameters of the subway rubber to be tested in the accelerated life test includes: Obtain the performance parameters of the door finger guard rubber and the hardness of the through-channel rubber; divide the performance parameters of the door finger guard rubber and the hardness of the through-channel rubber into several groups of test data, and mark each group of test data as i; where i = 1, 2,..., n, and n is the total number of groups of test data.

4. The life prediction method based on accelerated life test according to claim 1, characterized in that: The performing of curve fitting based on several groups of test data includes: A1: Extract several groups of test data corresponding to the elongation at break, tensile strength, and hardness of the door finger guard rubber in the test data, and several groups of test data corresponding to the hardness of the through-channel rubber; A2: Perform curve fitting on the elongation at break, tensile strength, and hardness of the door finger guard rubber respectively through a quadratic function to obtain several performance fitting functions; A3: Perform curve fitting on the hardness of the through-channel rubber through a quadratic function to obtain several performance fitting functions.

5. The life prediction method based on accelerated life test according to claim 3, characterized in that: The iteratively optimizing of each performance fitting function based on the coefficient of determination and mean square error includes: B1: Evaluate the fitting effect of the performance fitting function based on the coefficient of determination and mean square error to obtain an evaluation result; among them, the evaluation result includes a poor fitting effect and a good fitting effect; B2: Iteratively optimize the performance fitting function based on the evaluation result.

6. The life prediction method based on accelerated life test according to claim 5, characterized in that: The evaluating of the fitting effect of the performance fitting function based on the coefficient of determination and mean square error includes: C1: Extract several performance fitting functions corresponding to the performance parameters; C2: By formula Calculate the coefficient of determination R 2 ; Among them, y i is the i-th group of test data, is the predicted value of the fitting curve corresponding to the i-th group of test data, y mean is the average value of the test data; C3: By formula Calculate the mean square error MSE; C4: Set the coefficient of determination threshold as YZ1 and the mean square error threshold as YZ2; where the value range of YZ1 is 0 < YZ1 < 1, and the value range of YZ2 is 0 < YZ2 < 1; C5: Determine the coefficient of determination R 2 Check if it is > YZ1 and the mean squared error MSE < YZ2; if yes, mark the evaluation result as good fitting effect; if not, mark the evaluation result as poor fitting effect.

7. The life prediction method based on accelerated life test according to claim 5, characterized in that: The iteratively optimizing of the performance fitting function based on the evaluation result includes: D1: Extract the evaluation results corresponding to each performance fitting function; D2: Determine whether the evaluation result is a poor fitting effect; if yes, re-perform curve fitting on the corresponding performance fitting function and jump to D1; if no, retain the performance fitting function.

8. The life prediction method based on accelerated life test according to claim 1, characterized in that: The analyzing of the life of the subway rubber to be tested based on several performance fitting functions includes: E1: Obtain the initial performance value and current performance value of the subway rubber performance parameter to be tested; E2: Set the performance failure critical value SXJ corresponding to each performance parameter; the performance failure critical value is the ratio between the performance value of the subway rubber to be tested when it fails and the initial performance value; E3: By formula Calculate the failure assessment coefficient SPX; where XN0 is the initial performance value and XN is the current performance value; E4: Let the failure assessment coefficient be equal to the corresponding performance failure critical value, solve to obtain the performance value when the performance parameter of the subway rubber to be tested fails, and mark the performance value at the time of failure as the failure value; substitute the failure value into the corresponding performance fitting function to calculate the corresponding failure age; E5: Determine whether the failure evaluation coefficient of each performance parameter is less than the corresponding performance failure critical value; if yes, input the current value of the performance parameter into the corresponding performance fitting function to calculate the corresponding usage time, and jump to E6; if no, mark the life of the corresponding subway rubber to be tested as failure; E6: Subtract the usage time from the corresponding expiration date to get the remaining life, and mark the minimum value of the remaining life corresponding to each performance parameter as the remaining service life.

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