A Method for Evaluating the Reliability Index of a Mechanism with Extremely Small Samples

Abstract

An embodiment of the present application discloses a method and system for evaluating the reliability index of a mechanism with extremely small samples. The method includes: establishing a mechanism evolution simulation model according to the failure analysis results of the motion mechanism; calculating the life limit interval according to the life distribution form and life dispersion information of the motion mechanism; constructing a Bootstrap sampling distribution function according to the optimal life sample expansion data; constructing N random Bootstrap sub-samples of the Bootstrap sampling distribution function through random sampling, and calculating the lower limit of the average life; calculating the reliability index according to the lower limit of the average life. The proposed method uses reliability simulation analysis to obtain the life distribution type and life dispersion of the motion mechanism, uses the test life to obtain the limit distribution interval of the product life, and then through the optimized virtual augmented sample method and the modified Bootstrap method, can make full use of the existing information to efficiently and accurately infer the reliability level index of the motion mechanism, with wide applicability.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of industrial technologies, and particularly to a method and a system for evaluating the reliability index of a mechanism with extremely small samples. Background Art

[0002] For complex mechanism products with high test costs and long test times, generally only extremely small-sample tests (k ≤ 3) can be carried out during the reliability evaluation process. For such engineering problems, the reliability evaluation method based on virtual augmentation is very effective for solving extremely small-sample engineering problems.

[0003] However, the traditional virtual augmentation method has a large subjective factor, and there is no corresponding index to measure the effect of expanding samples by the virtual augmentation sample method. In addition, the variance σ selected during the virtual augmentation process is from a rough value of engineering statistics and is not well applicable to specific products. In addition to the deficiencies of the traditional virtual augmentation method, there are also many drawbacks in traditional Bootstrap, such as the resampled samples cannot break through the limit range of the small samples generated by the virtual augmentation method, and the expectations of the sampling results of some improved Bootstrap methods deviate from the expectations of the virtual augmentation samples. Summary of the Invention

[0004] Aiming at the problem of extremely small sample sizes for reliability evaluation caused by high test costs and long test times for complex mechanisms, the embodiments of the present application propose a method and a system for evaluating the reliability index of a mechanism with extremely small samples. By using reliability simulation analysis to obtain the life distribution type and life dispersion degree of mechanical products, using the test life to obtain the extreme distribution interval of the product life, and then through an optimized virtual augmentation sample method and a modified Bootstrap method, the existing information is fully utilized to infer the reliability level index of mechanical products. This method has the characteristics of wide applicability, reasonable and credible results, etc.

[0005] To achieve the above object, the embodiments of the present application provide the following technical solutions:

[0006] According to the first aspect of the embodiments of the present application, a method for evaluating the reliability index of a mechanism with extremely small samples is provided, and the method includes:

[0007] Obtain the life data of the motion mechanism;

[0008] Establish an institutional evolution simulation model according to the failure analysis result of the motion mechanism, and calculate the life distribution form and life dispersion degree information of the motion mechanism;

[0009] Calculate the life limit interval according to the life distribution form and life dispersion degree information of the motion mechanism;

[0010] Calculate the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval;

[0011] Construct a Bootstrap sampling distribution function according to the best life sample expansion data;

[0012] Construct N random Bootstrap subsamples of the Bootstrap sampling distribution function by random sampling and calculate the lower limit of the average life; N is an integer greater than 1×10 4 ;

[0013] Calculate the reliability index according to the lower limit of the average life.

[0014] Optionally, establish an institutional evolution simulation model according to the failure analysis results of the motion mechanism, including:

[0015] Conduct a failure analysis on the motion mechanism to obtain the failure mode and influencing factor parameters of the motion mechanism;

[0016] Establish a dynamic simulation model of the motion mechanism according to the motion principle and component parts of the motion mechanism;

[0017] Establish a degradation model of the motion mechanism based on the dynamic simulation model according to the failure mode of the motion mechanism.

[0018] Optionally, calculate the life distribution form and life dispersion information of the motion mechanism, including:

[0019] Input the random distribution parameters of the influencing factors into the degradation model of the motion mechanism, conduct a simulation calculation of the motion mechanism life, obtain the life distribution of the motion mechanism, and obtain the dispersion parameter of the motion mechanism life.

[0020] Optionally, calculate the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval, including:

[0021] Construct a linear empirical distribution function of life points with virtual augmented samples;

[0022] Construct a pseudo-cumulative life distribution function of the motion mechanism according to the life distribution form and life dispersion information and test life value of the motion mechanism;

[0023] Obtain the difference between the linear empirical distribution function value and the pseudo-cumulative life distribution function value within the entire life limit interval according to the life limit interval, and use the difference as the optimization target;

[0024] Calculate the optimal sample expansion coefficient and the best life sample expansion data through the optimization target and constraint conditions.

[0025] Optionally, constructing N random Bootstrap subsamples of the Bootstrap sampling distribution function through random sampling and calculating the lower limit of the average life includes:

[0026] Constructing a random Bootstrap subsample of the distribution function through random sampling;

[0027] Repeating the above steps to generate Bootstrap subsamples with N sample numbers and calculating the life mean distribution;

[0028] Calculating the lower limit of the average life according to the life mean distribution.

[0029] Optionally, constructing a random Bootstrap subsample of the distribution function through random sampling includes:

[0030] Generating random numbers within a uniform distribution interval and generating life sample data corresponding to each random number according to the distribution function;

[0031] Repeating the above steps several times until a set of Bootstrap subsamples is obtained;

[0032] Calculating the mean of the set of Bootstrap subsamples as the life mean sample of the set of Bootstrap subsamples.

[0033] Optionally, calculating the lower limit of the average life according to the life mean distribution includes:

[0034] Obtaining the confidence interval of the life mean based on the interval estimation method according to the life mean distribution and solving the lower limit of the average life at the quantile point.

[0035] According to the second aspect of the embodiments of the present application, a minimum sample reliability index evaluation system for an institution is provided, and the system includes:

[0036] A data acquisition module for acquiring the life data of a motion mechanism;

[0037] A life distribution module for establishing an institution evolution simulation model according to the failure analysis result of the motion mechanism and calculating the life distribution form and life dispersion degree information of the motion mechanism;

[0038] A life limit interval calculation module for calculating a life limit interval according to the life distribution form and life dispersion degree information of the motion mechanism;

[0039] A life sample expansion data calculation module for calculating an optimal sample expansion coefficient and optimal life sample expansion data according to the life limit interval;

[0040] A distribution function calculation module for constructing a Bootstrap sampling distribution function according to the optimal life sample expansion data;

[0041] The lower limit calculation module of the sample average life is used to construct N random Bootstrap subsamples of the Bootstrap sampling distribution function through random sampling and calculate the lower limit of the average life;

[0042] The reliability index calculation module is used to calculate the reliability index according to the lower limit of the average life.

[0043] According to the third aspect of the embodiments of the present application, an electronic device is provided, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor runs the computer program, the method described in the first aspect above is implemented.

[0044] According to the fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described in the first aspect above.

[0045] In summary, the embodiments of the present application propose a method and system for evaluating the reliability index of a mechanism with extremely small samples. By obtaining the life data of a moving mechanism; establishing an institutional evolution simulation model according to the failure analysis results of the moving mechanism, and calculating the life distribution form and life dispersion information of the moving mechanism; calculating the life limit interval according to the life distribution form and life dispersion information of the moving mechanism; calculating the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval; constructing a Bootstrap sampling distribution function according to the best life sample expansion data; constructing N random Bootstrap subsamples of the Bootstrap sampling distribution function through random sampling, and calculating the lower limit of the average life; calculating the reliability index according to the lower limit of the average life. The proposed method uses reliability simulation analysis to obtain the life distribution type and life dispersion of the moving mechanism, uses the test life to obtain the limit distribution interval of the product life, and then through the optimized virtual augmented sample method and the modified Bootstrap method, can make full use of the existing information to efficiently and accurately infer the reliability level index of the moving mechanism, with wide applicability. Description of the Drawings

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained according to the provided drawings.

[0047] The structures, proportions, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0048] Figure 1 It is a schematic flowchart of a method for evaluating the reliability index of a minimal sample of an institution provided by an embodiment of the present application;

[0049] Figure 2 It is a flowchart of a method for evaluating the reliability of a minimal sample provided by an embodiment of the present application;

[0050] Figure 3 It is a schematic diagram of the life limit interval provided by an embodiment of the present application;

[0051] Figure 4a and 4b It is a composition structure diagram of a cabin door lock mechanism provided by an embodiment of the present application;

[0052] Figure 5 It is a schematic diagram of the failure of insufficient closing accuracy of a cabin door lock provided by an embodiment of the present application;

[0053] Figure 6 It is a simulation life distribution diagram of a cabin door lock provided by an embodiment of the present application;

[0054] Figure 7 It is a comparison diagram of the best augmented sample and the traditional virtual augmented sample provided by an embodiment of the present application;

[0055] Figure 8 It is a distribution diagram of the life mean value of a cabin door lock provided by an embodiment of the present application;

[0056] Figure 9 It is a block diagram of a system for evaluating the reliability index of a minimal sample of an institution provided by an embodiment of the present application;

[0057] Figure 10 It shows a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0058] Figure 11 It shows a schematic diagram of a computer-readable storage medium provided by an embodiment of the present application. Specific embodiments

[0059] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0060] The problem of degradation and failure of complex mechanism products is prominent, such as wear of kinematic pairs, stress relaxation of spring parts, aging of rubber parts, etc., which will cause the problem of system performance degradation during the long-term operation of the mechanism. When the failure threshold is reached, the product is considered to fail.

[0061] Figure 1 An evaluation method for the reliability index of a minimal sample of a mechanism provided by an embodiment of the present application is shown. This method is suitable for kinematic mechanism products whose life follows a normal distribution or a lognormal distribution. The method includes:

[0062] Step 101: Obtain the life data of the kinematic mechanism;

[0063] Step 102: Establish an institutional evolution simulation model according to the failure analysis result of the kinematic mechanism, and calculate the life distribution form and life dispersion information of the kinematic mechanism;

[0064] Step 103: Calculate the life limit interval according to the life distribution form and life dispersion information of the kinematic mechanism;

[0065] Step 104: Calculate the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval;

[0066] Step 105: Construct a Bootstrap sampling distribution function according to the best life sample expansion data;

[0067] Step 106: Construct N random Bootstrap subsamples of the Bootstrap sampling distribution function by random sampling, and calculate the lower limit of the average life; N is an integer greater than 1×10 4 ;

[0068] Step 107: Calculate the reliability index according to the lower limit of the average life.

[0069] In a possible implementation manner, in step 102, establishing an institutional evolution simulation model according to the failure analysis result of the kinematic mechanism includes:

[0070] Perform a failure analysis on the motion mechanism to obtain the failure mode and influencing factor parameters of the motion mechanism; establish a dynamic simulation model of the motion mechanism according to the motion principle and component parts of the motion mechanism; establish an institutional evolution simulation model of the motion mechanism based on the failure mode of the motion mechanism and the dynamic simulation model.

[0071] In a possible implementation manner, calculating the life distribution form and life dispersion degree information of the motion mechanism includes:

[0072] Input the random distribution parameters of the influencing factors into the degradation model of the motion mechanism, perform simulation calculations on the life of the motion mechanism, obtain the life distribution of the motion mechanism, and acquire the dispersion degree parameters of the life of the motion mechanism.

[0073] In a possible implementation manner, in step 104, calculating the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval includes:

[0074] Construct a linear empirical distribution function of life points with virtual augmented samples; construct a pseudo-cumulative life distribution function of the motion mechanism according to the life distribution form and life dispersion degree information of the motion mechanism and the test life value; obtain the difference between the linear empirical distribution function value and the pseudo-cumulative life distribution function value within the entire life limit interval according to the life limit interval, and use the difference as the optimization target; calculate the optimal sample expansion coefficient and the best life sample expansion data through the optimization target and constraint conditions.

[0075] In a possible implementation manner, in step 106, constructing N random Bootstrap subsamples of the Bootstrap sampling distribution function by random sampling and calculating the lower limit of the average life includes:

[0076] Construct random Bootstrap subsamples of the distribution function by random sampling; repeat the above steps to generate Bootstrap subsamples with N sample numbers, and calculate the life mean distribution; calculate the lower limit of the average life according to the life mean distribution.

[0077] In a possible implementation manner, constructing random Bootstrap subsamples of the distribution function by random sampling includes:

[0078] Generate random numbers within the uniform distribution interval, and generate life sample data corresponding to each random number according to the distribution function; repeat the above steps several times until a set of Bootstrap subsamples is obtained; calculate the mean of this set of Bootstrap subsamples as the life mean sample of this set of Bootstrap subsamples.

[0079] In a possible implementation manner, calculating the lower limit of the average life according to the average life distribution includes:

[0080] Based on the interval estimation method, obtaining the confidence interval of the average life according to the average life distribution, and solving the lower limit of the average life at the quantile.

[0081] The following will describe in detail a method for evaluating the reliability index of a minimal sample of a mechanism provided in an embodiment of the present application with reference to the accompanying drawings.

[0082] Figure 2 The flow of the reliability evaluation method for a minimal sample provided in the embodiment of the present application is shown, and the specific steps are as follows:

[0083] Step 1: According to the working principle and function of the complex mechanism product, design a performance degradation test for the minimal sample of the mechanical product to obtain the performance degradation law of the mechanism and the test life of the mechanism under the minimal sample:

[0084] {t 1 , t 2 , …, t k}(k ≤ 3).

[0085] Step 2: Establish an evolutionary simulation model for the complex mechanism product, input the randomness of the product influencing factors, and through simulation reliability analysis, calculate the life distribution form and life dispersion information of the product. Specifically, it includes:

[0086] Step 2.1: Conduct a failure analysis on the complex mechanism product to obtain the failure mode and important influencing factors of the product.

[0087] Step 2.2: According to the motion principle and component composition of the mechanism, create a multi-body dynamics simulation model of the mechanism. Conduct simulation on the model and compare and verify it with the dynamic data detected by physical tests. If the accuracy meets the requirements, continue to execute; otherwise, modify and improve the model. Simulation modeling analysis and verification are not the focus of this method and will not be elaborated here.

[0088] Step 2.3: According to the degradation failure mode of the product, create a degradation model of the mechanism based on the multi-body dynamics simulation model.

[0089] Step 2.4: Perform parametric processing on the important influencing factors of the model and input the random distribution parameters of the influencing factors.

[0090] Step 2.5: Conduct large-sample simulation calculations of the mechanism life to obtain the life distribution of the complex mechanical product and obtain the dispersion parameters of the mechanism life.

[0091] If the product follows a normal distribution or a lognormal distribution, the subsequent evaluation process and calculation formulas for the product can be fully implemented according to this method. If the product follows other distributions such as Weibull distribution, this method is still applicable for the subsequent evaluation process, but some formulas need to be derived according to the characteristics of the Weibull distribution.

[0092] If the simulation results follow a normal distribution N(t sim ,σ 0 ), it means that the product life follows a normal distribution, and the dispersion of the mechanism life is σ 0 . If the simulation results follow a lognormal distribution logt~N(t sim ,σ 0 ), it means that the product life follows a lognormal distribution. All the lives in the subsequent methods refer to the log-lives, and the dispersion of the mechanism life is σ 0 .

[0093] Step 3: Calculate the life limit interval [x l ,x u of the mechanical product, that is, it is considered that the product sample life will not fall outside the interval. Figure 3 The schematic diagram of the life limit interval is shown.

[0094] Arrange the life values {t 1 ,t 2 ,…,t k} of k (k≤3) samples in the physical test in ascending order {t (1) ,t (2) ,…,t (k)}. According to the "principle of small probability events", at a certain confidence level 1-α 0 (α 0 ≤0.01), it is considered that the sample values will not fall outside the confidence interval [θ 1 ,θ 2 of the mechanism life.

[0095] Step 3.1: Calculate the lower limit value x l of the product life;

[0096] Assume that the maximum life data t (n) in the test is located at the upper quantile of the life interval (i.e., the upper limit θ 2 of the confidence interval), and the corresponding value (i.e., the lower limit θ 1 of the confidence interval) can be obtained. This value is the lower limit x l of the life. If the mechanism life follows a normal distribution, the standard deviation is σ 0 , and the specific calculation formula is as follows:

[0097]

[0098]

[0099] Step 3.2: Calculate the upper limit value x of the life of the mechanism product u ;

[0100] Similarly, assume that the minimum life data t in the test (1) is located at the lower quantile of the life interval (i.e., the lower limit θ of the confidence interval 1 ), and the corresponding value (i.e., the upper limit θ of the confidence interval 2 ) can be obtained. This value is the upper limit of life x u . The specific calculation formula for the normal distribution is as follows:

[0101]

[0102]

[0103] Obtain the limit interval of the product life [x l , x u .

[0104] Step 4: Solve the optimal sample expansion coefficients a and b to obtain the best life sample expansion data {T (1) , T (2) ,..., T (n)}.

[0105] Expand the samples to be determined by the sample virtual augmentation method. The criterion of the virtual augmentation method is:

[0106] (1) The mean of the augmented subsample should be equal to the mean of the original subsample;

[0107] (2) The standard deviation of the augmented subsample should be equal to the standard deviation followed by the original subsample products.

[0108] When the mechanism life distribution follows a normal distribution, the sample can be directly expanded using the following formula.

[0109]

[0110]

[0111]

[0112] i = 1, 2,..., m 2

[0113] Where: is the mean life of the mechanism in the small sample test, that is Augment the sample life data to n (n > 10 and n is odd), m = n - 1, m 2 = m / 2,

[0114] ξ is a coefficient related to a and b, and the solution formula is as follows:

[0115]

[0116] The augmented sample sequence is obtained as {T (1) , T (2) ,..., T (n)}.

[0117] a and b are control coefficients and also design variables to be optimized. The best results need to be obtained through the following optimization steps to achieve the best sample augmentation effect.

[0118] Step 4.1: Construct the linear empirical distribution function Y1 of the life points with virtual augmented samples.

[0119]

[0120] Step 4.2: Based on the life distribution form and dispersion information obtained from the simulation results, combined with the experimental life values, construct the pseudo-cumulative life distribution function Y 2 (t).

[0121] When the life distribution of the mechanism satisfies the normal distribution in the reliability analysis, its pseudo-cumulative life distribution function Y 2 (t) is the normal distribution function truncated by the interval [x l , x u where is the life mean of the mechanism in the minimum sample test, and σ 0 is the life dispersion in the simulation analysis.

[0122] Step 4.3: Divide the mechanism life limit interval [x l , x u into q parts, and find the square of the difference between the two function values of Y 1 (t) and Y 2 (t) in each interval. Sum the squares of the differences in all sections to obtain the difference ε between the two functions in the entire limit interval, and take ε as the optimization objective.

[0123]

[0124] Step 4.4 Design and solve the optimization problem.

[0125] Optimization objective: min ε(a, b) ​

[0126] Constraints: a > 0, b > 0

[0127]

[0128] x 1 < T (1)

[0129] x u ≥ T (n)

[0130] Through optimized calculation, the optimal sample expansion coefficients a and b are obtained; then the best expanded samples {T (1) , T (2) ,..., T (n)} are obtained from the expansion formula.

[0131] Step 5: Construct the distribution function F(x) based on the results of the expanded samples.

[0132]

[0133] Where: θ is the proportionality parameter, satisfying the following range:

[0134]

[0135] Step 6: Simulate and generate random samples that follow the empirical cumulative distribution function F(x), that is, Bootstrap subsamples; the specific method is as follows:

[0136] Step 6.1: Generate a random number η in the uniform distribution U[0, 1] interval;

[0137] Step 6.2: Generate the lifetime sample data x corresponding to each random number according to the distribution function F(x) F (η). Let η = F[x F , and the solved value is the corresponding lifetime sample data x F (η);

[0138] If the theoretical results x F (η i ) corresponding to the first and third segments of the F(x) function are not easy to obtain, a numerical approximate solution can be obtained through the following method.

[0139] (1) Generate M equally spaced points of the independent variable x in the interval [x l , T (1) , calculate the function values corresponding to each independent variable according to the distribution function F(x), and use the M independent variables and the corresponding function values as the data set A1 for standby.

[0140] Similarly, in the interval [T (n) , x uGenerate M equally spaced points of the independent variable x, calculate the function values corresponding to each independent variable according to the distribution function F(x), and use the M independent variables and their corresponding function values as the dataset A3 for backup.

[0141] (2) If Search for the function value F(x q ) closest to η in the dataset A1, and the corresponding x q is the numerical approximate solution x F of the required life sample point.

[0142] If x F (η) = T (i) +(β - i)T (i+1) -T (i) ); where: β = (n + 1)η, i = [β].

[0143] If Search for the function value F(x q ) closest to η in the dataset A3, and the corresponding x q is the numerical approximate solution x F of the required life sample point.

[0144] Step 6.3: Repeat the above steps n times to obtain a set of Bootstrap subsamples {x F1 , x F2 ,..., x Fn},

[0145] Step 6.4: Calculate the mean of this set of Bootstrap subsamples, and obtain the life mean of this set of Bootstrap subsamples as a sample.

[0146] Step 7: Repeat the operation in Step 6 N times (N ≥ 10000) to obtain the distribution of N life means, and then obtain the lower limit of the average life.

[0147] As the number of samplings increases, the distribution of the life mean gradually approaches the normal distribution, and the distribution of the mean life can be obtained Then, by the interval estimation method, the confidence interval of the fatigue life mean of the mechanical product can be obtained, and the lower limit of the average life at the quantile α can be solved where 1 - α is the confidence level.

[0148] Step 8: Calculate the reliability index. Use the lower limit of the average life as the estimate of the product mean life, and the reliable life of the product at the corresponding reliability can be calculated by the following formula.

[0149]

[0150] where γ is the specified reliability.

[0151] In the existing literature, regarding the extremely small sample reliability evaluation method for large aviation mechanical systems, either only analyzing small sample test data, the evaluation results are too conservative due to incomplete information; or using a rough estimate of the mechanical product life distribution as a reference for unknown parameters, and the evaluation results are hardly convincing. The method proposed in this paper uses simulation means to obtain the distribution law and dispersion parameters of mechanical products from the random distribution of influencing factors, and then fully utilizes the results of extremely small sample tests. By using the modified Bootstrap method, the lower limit of the product life mean value is obtained, and further the reliable life result that is safe in engineering is obtained. And this method has been applied in engineering, proving its feasibility.

[0152] The following is an example of an extremely small sample reliability index evaluation method for a mechanism provided in an embodiment of the present application. Taking a certain type of aircraft cabin door lock as an example, its main function is to complete the opening and closing of the lock through instructions, realizing the locking task after the cabin door system is closed and the unlocking task before the door is opened.

[0153] First, briefly introduce the composition structure and movement principle of the cabin door lock:

[0154] The cabin door lock mechanism is composed of an actuator assembly (actuator and piston rod), a connecting rod assembly (rocker ABD, connecting rod BC, connecting rod DE, and lock hook), and a spring assembly, etc. Figure 4a and Figure 4b That is the composition structure of the cabin door lock mechanism.

[0155] Among them, the actuator assembly is the power assembly of the lock mechanism. The piston rod is driven to move by hydraulic pressure, and then the connecting rod assembly can be further driven to move according to the design law. During the unlocking process, under the action of hydraulic pressure, the piston rod moves to the right along the actuator, and the lock hook rotates counterclockwise under the drive of the connecting rod assembly to complete the opening function of the cabin door lock; during the locking process, under the action of the opposite hydraulic pressure, the piston rod moves to the left along the actuator, and the lock hook rotates clockwise under the drive of the connecting rod assembly to complete the closing and locking functions of the cabin door lock.

[0156] The first stage, the cabin door lock life test.

[0157] According to factors such as the function and working environment of the cabin door lock, design a life test plan, and conduct a life test on 1 cabin door lock with the test sample number, and obtain the test life of the cabin door lock as t 1 = 24390 opening and closing cycles.

[0158] The second stage, the reliability evolution simulation modeling and analysis of the cabin door lock.

[0159] 2.1 Failure analysis of the cabin door lock.

[0160] During the long-term opening and closing operation of the cabin door lock, problems such as insufficient closing position accuracy may occur. If the deflection angle δ of the front end of the locking hook is far from the target position δ 0 is greater than 1.5°, it will cause the locking hook and the locking ring to fail to lock, and it is considered that the cabin door lock mechanism fails due to insufficient closing position accuracy. Figure 5 This is the schematic diagram of the failure of the cabin door lock due to insufficient closing position accuracy.

[0161] The main factors affecting the insufficient closing position accuracy include the lengths of the rods of the connecting rod assembly and the clearances of the various kinematic pairs. The main reasons for the failure of insufficient closing position accuracy are the wear of the mechanism hinges and the relaxation of the spring stress.

[0162] 2.2 Establish the dynamic model of the cabin door lock.

[0163] According to the motion principle of the cabin door lock, establish a dynamic model. Conduct a dynamic test on the simulation model of the cabin door lock. Compare the force data of the locking hook of the cabin door lock measured by the test with the simulation results of the model. The change laws of the simulation and test results are consistent during the entire opening and closing process, and the accuracy of the cabin door lock model meets the requirements.

[0164] 2.3 Establish the degradation model of the cabin door lock.

[0165] ① Embed the hinge wear model into the dynamic model of the lock mechanism based on the Archard model;

[0166] ② According to the accelerated degradation test of the spring, the formula for the loss rate of the spring stiffness coefficient due to stress relaxation with the calendar cycle is obtained as: Embed the formula into the dynamic model.

[0167] 2.4 Input the random distribution of influencing factors.

[0168] According to the design tolerances and material properties of the cabin door lock parts, input the random distribution parameters of the influencing factors, as shown in Table 1.

[0169] Table 1 Random distribution parameter table of each influencing factor

[0170]

[0171] 2.5 Simulation life distribution results of the cabin door lock.

[0172] Using the Monte Carlo method, the simulation life of the product with 10 6 different combinations of input parameters is analyzed and obtained. Attached Figure 6 This is the simulation life distribution diagram of the cabin door lock. It is obtained that the life of the cabin door lock follows a normal distribution, where the life dispersion, that is, the standard deviation σ 0 = 1922.

[0173] In the third stage, calculate the life limit interval [x l , xu .

[0174] Since there is only one test sample, select the confidence factor α 0 to be 0.0001, then the confidence level 1 - α 0 = 0.9999, and the calculation is as follows:

[0175]

[0176]

[0177]

[0178] Then the limit life interval of the cabin door lock mechanism is [9434.5, 39345.6].

[0179] In the fourth stage, obtain the optimal life expansion sample.

[0180] Take the expansion sample quantity n = 11, and through optimization calculation, the optimal expansion coefficients are a = 0.01 and b = 3.95. Figure 7 This is the comparison chart of the optimal expansion sample and the traditional virtual augmented expansion sample, where the expansion coefficient selected by the conventional virtual augmentation method is a 0 = 0.2, b 0 = 1.3.

[0181] Then the expansion sample result is [10711.29, 21049.91, 22842.68, 22906.43, 23827.87, 24390, 24952.13, 25873.57, 25937.32, 27730.09, 38068.71].

[0182] In the fifth stage, the Bootstrap sampling distribution function form of the cabin door lock mechanism is:

[0183]

[0184] where θ = 140.

[0185] In the sixth stage, conduct Bootstrap sampling.

[0186] Extract the number of Bootstrap subsamples N = 5 × 10 4 , Figure 8 This is the distribution result of the cabin door lock life mean, and from this, the distribution law of the cabin door lock life mean is obtained: the life mean approximately follows a normal distribution N(24406.67, 2060.32).

[0187] In the seventh stage, the reliability assessment result.

[0188] Take the confidence level as 0.9 to obtain the lower limit of the life mean

[0189] Take the reliability γ = 0.999 to obtain the reliable life T(0.999) = 15799 opening and closing cycles.

[0190] In summary, the embodiment of the present application proposes a method for evaluating the reliability index of a mechanism with extremely small samples. By obtaining the life data of the moving mechanism; establishing an institutional evolution simulation model according to the failure analysis results of the moving mechanism, and calculating the life distribution form and life dispersion information of the moving mechanism; calculating the life limit interval according to the life distribution form and life dispersion information of the moving mechanism; calculating the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval; constructing a Bootstrap sampling distribution function according to the best life sample expansion data; constructing N random Bootstrap subsamples of the Bootstrap sampling distribution function through random sampling, and calculating the lower limit of the average life; calculating the reliability index according to the lower limit of the average life. The proposed method uses reliability simulation analysis to obtain the life distribution type and life dispersion of the moving mechanism, uses the test life to obtain the limit distribution interval of the product life, and then through the optimized virtual augmented sample method and the modified Bootstrap method, it can make full use of the existing information to efficiently and accurately infer the reliability level index of the moving mechanism, with wide applicability.

[0191] Based on the same technical concept, the embodiment of the present application also provides a system for evaluating the reliability index of a mechanism with extremely small samples, as Figure 9 shown, the system includes:

[0192] A data acquisition module 901, configured to acquire the life data of the moving mechanism;

[0193] A life distribution module 902, configured to establish an institutional evolution simulation model according to the failure analysis results of the moving mechanism, and calculate the life distribution form and life dispersion information of the moving mechanism;

[0194] A life limit interval calculation module 903, configured to calculate the life limit interval according to the life distribution form and life dispersion information of the moving mechanism;

[0195] A life sample expansion data calculation module 904, configured to calculate the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval;

[0196] A distribution function calculation module 905, configured to construct a Bootstrap sampling distribution function according to the best life sample expansion data;

[0197] The lower limit of the sample average life calculation module 906 is used to construct N random Bootstrap subsamples of the Bootstrap sampling distribution function through random sampling and calculate the lower limit of the average life;

[0198] The reliability index calculation module 907 is used to calculate the reliability index according to the lower limit of the average life.

[0199] The embodiment of the present application also provides an electronic device corresponding to the method provided in the foregoing embodiment. Please refer to Figure 10 , which shows a schematic diagram of an electronic device provided in some embodiments of the present application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected through the bus 202; a computer program that can run on the processor 200 is stored in the memory 201, and when the processor 200 runs the computer program, it executes the method provided in any of the foregoing embodiments of the present application.

[0200] Among them, the memory 201 may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one physical port 203 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0201] The bus 202 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 201 is used to store the program, and after receiving the execution instruction, the processor 200 executes the program. The method disclosed in any of the foregoing embodiments of the present application can be applied to the processor 200 or implemented by the processor 200.

[0202] The processor 200 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 200 or the instructions in the form of software. The above-mentioned processor 200 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by the hardware decoding processor, or executed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and combines its hardware to complete the steps of the above method.

[0203] The electronic device provided by the embodiments of the present application and the method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by it.

[0204] The embodiments of the present application also provide a computer-readable storage medium corresponding to the method provided by the foregoing embodiments. Please refer to Figure 11 which shows that the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method provided by any of the foregoing embodiments.

[0205] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here one by one.

[0206] The computer-readable storage medium provided by the above embodiments of the present application and the method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored in it.

[0207] It should be noted that:

[0208] The algorithms and displays provided herein are not inherently related to any particular computer, virtual apparatus, or other device. A variety of general-purpose apparatuses may also be used in conjunction with the teachings presented herein. The structure required to construct such apparatuses will be apparent from the above description. In addition, the present application is not directed to any particular programming language. It should be understood that the content of the present application described herein can be implemented using a variety of programming languages, and the description of a particular language above is for the purpose of disclosing the best mode of the present application.

[0209] In the specification provided herein, a number of specific details are set forth. However, it can be understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0210] Similarly, it should be understood that in order to streamline the present application and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed present application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate embodiment of the present application.

[0211] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except for the fact that at least some of such features and / or processes or units are mutually exclusive, any combination can be adopted for all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0212] In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments means that it is within the scope of this application and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0213] Each component embodiment of this application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation device according to the embodiments of this application. This application can also be implemented as a device or device program (such as a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0214] It should be noted that the above embodiments illustrate rather than limit this application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

[0215] As described above, the above are only the preferred specific embodiments of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by this application should be covered by the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claimed rights.

Claims

1. A method for evaluating the reliability index of a mechanism with extremely small samples, characterized in that, the method includes: Obtain the life data of the moving mechanism; Establish an institutional evolution simulation model based on the failure analysis results of the moving mechanism, and calculate the life distribution form and life dispersion information of the moving mechanism; Calculate the life limit interval according to the life distribution form and life dispersion information of the moving mechanism; Calculate the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval, including: Construct a linear empirical distribution function of life points with virtual augmented samples; Construct the pseudo-cumulative life distribution function of the moving mechanism according to the life distribution form, life dispersion information and test life value of the moving mechanism; Obtain the difference between the linear empirical distribution function value and the pseudo-cumulative life distribution function value within the entire life limit interval according to the life limit interval, and use the difference as the optimization target; Calculate the optimal sample expansion coefficient and the best life sample expansion data through the optimization target and constraint conditions, Construct a Bootstrap sampling distribution function according to the best life sample expansion data; Construct by random sampling N random Bootstrap subsamples of the said Bootstrap sampling distribution function, and calculate the lower limit of the average life; N is an integer greater than 1×10 4 ; Calculate the reliability index according to the lower limit of the average life.

2. The method according to claim 1, characterized in that, Establishing an institutional evolution simulation model based on the failure analysis results of the moving mechanism includes: Conduct a failure analysis on the moving mechanism to obtain the failure mode and influencing factor parameters of the moving mechanism; Establish a dynamic simulation model of the moving mechanism according to the motion principle and component parts of the moving mechanism; Establish a degradation model of the moving mechanism based on the failure mode of the moving mechanism and the dynamic simulation model.

3. The method according to claim 1 or 2, characterized in that, Calculating the life distribution form and life dispersion information of the moving mechanism includes: Input the random distribution parameters of the influencing factors into the degradation model of the moving mechanism, conduct simulation calculations of the moving mechanism life, obtain the life distribution of the moving mechanism, and obtain the dispersion parameters of the moving mechanism life.

4. The method according to claim 1, characterized in that, Construct by random sampling N random Bootstrap subsamples of the said Bootstrap sampling distribution function, and calculate the lower limit of the average life, including: Construct a random Bootstrap subsample of the distribution function through random sampling; Repeat the above steps to generate N Bootstrap subsamples with the number of samples, and calculate the distribution of life means; Calculate the lower limit of the average life according to the life mean distribution.

5. The method according to claim 4, characterized in that, Constructing a random Bootstrap subsample of the distribution function through random sampling includes: Generate random numbers within a uniform distribution interval, and generate life sample data corresponding to each random number according to the distribution function; Repeat the above steps several times until a set of Bootstrap subsamples is obtained; Calculate the mean of this set of Bootstrap subsamples as the life mean sample of this set of Bootstrap subsamples.

6. The method according to claim 4, characterized in that, Calculating the lower limit of the average life according to the life mean distribution includes: Obtain the confidence interval of the life mean based on the interval estimation method according to the life mean distribution, and solve the lower limit of the average life at the quantile.

7. A system for evaluating the reliability index of a mechanism with extremely small samples, characterized in that, the system includes: A data acquisition module for acquiring the life data of a motion mechanism; A life distribution module for establishing a mechanism evolution simulation model based on the failure analysis result of the motion mechanism, and calculating the life distribution form and life dispersion degree information of the motion mechanism; A life limit interval calculation module for calculating the life limit interval according to the life distribution form and life dispersion degree information of the motion mechanism; A life sample expansion data calculation module for calculating the optimal sample expansion coefficient and the best life sample expansion data according to the life limit interval, including: constructing a linear empirical distribution function of life points with virtual augmented samples; Constructing a pseudo-cumulative life distribution function of the motion mechanism according to the life distribution form and life dispersion degree information and the test life value of the motion mechanism; Obtaining the difference between the linear empirical distribution function value and the pseudo-cumulative life distribution function value within the entire life limit interval according to the life limit interval, and using the difference as the optimization target; Calculating the optimal sample expansion coefficient and the best life sample expansion data through the optimization target and constraint conditions; A distribution function calculation module for constructing a Bootstrap sampling distribution function according to the best life sample expansion data; The lower limit calculation module of the sample average life is used to construct N random Bootstrap subsamples of the Bootstrap sampling distribution function and calculate the lower limit of the average life; A reliability index calculation module for calculating the reliability index according to the lower limit of the average life.

8. An electronic device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor runs the computer program, the method according to any one of claims 1-6 is implemented.

9. A computer-readable storage medium, characterized in that, Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1-6.

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