Initiating explosive device output reliability evaluation method based on belief statistical inference

Abstract

The invention discloses an initiating explosive device output reliability evaluation method based on belief statistical inference, and the method comprises the following steps: S1, calculating a mean value # imgabs0 # and a variance S2 according to n pieces of output parameter test data (x1, x2,..., xn); s2, a t distribution random number with the degree of freedom of (n-1) and a x2 distribution random number with the degree of freedom of (n-1) are generated and recorded as t1, t2,..., tl, z1, z2,..., zl respectively, and l is larger than or equal to 100000; s3, calculating the reliability according to the mean value # imgabs 1 # and the variance S2, ti, zi, i = 1, 2,..., l; s4, if the confidence coefficient is gamma, k is equal to 1 (1-gamma), and the value corresponding to R (k) is the lower limit value of reliability evaluation; the problems that in an existing initiating explosive device output reliability evaluation method, statistical data are lacked, the required sample size is large, and the evaluation precision is not high are solved.

Description

Technical Field

[0001] The invention relates to the field of reliability assessment, and in particular to a method for assessing the output reliability of explosive devices based on belief statistical inference. Background Art

[0002] Explosives are key components of weapon systems. Their output reliability is crucial to the normal functioning of the entire system and is directly related to the system's combat effectiveness and mission success. If any pyrotechnic cannot be reliably detonated, it may cause the weapon to fail to hit the target or fail to effectively damage the target, miss the opportunity, and even put the own troops in danger. Therefore, it is particularly important to evaluate its output reliability.

[0003] Existing output reliability assessment methods require a large amount of statistical data to determine the probability distribution function of the output parameters and establish a more accurate calculation model in order to accurately assess its output reliability. However, this is often not met when actually conducting an output reliability assessment of pyrotechnics. The reason for this problem is that, on the one hand, due to the high value of pyrotechnics, it is difficult or impossible to obtain a large amount of statistical data; on the other hand, it is generally assumed that the output parameters follow a normal distribution for statistical inference. When the sample size is small, the estimation accuracy of the normal distribution parameters is not high, which in turn affects the correctness of the assessment results.

[0004] In view of the above reasons, in order to more effectively deal with the problems of insufficient sample size of statistical data and low accuracy of distribution parameter distribution model, a reliability evaluation method for pyrotechnic output based on belief statistical inference is proposed. This method directly starts from sample data and infers unknown parameters by constructing belief distribution. It can make full use of the information contained in the sample and more comprehensively reflect the characteristics of the data. It can more effectively use sample data, thereby improving the accuracy and reliability of inference. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a method for evaluating the reliability of pyrotechnic output based on belief statistical inference, which solves the problems of lack of statistical data, large sample size required, and low evaluation accuracy in the existing pyrotechnic output reliability evaluation methods.

[0006] The technical solution provided by the present invention is as follows:

[0007] A reliability evaluation method for pyrotechnic output based on belief statistical inference includes the following steps:

[0008] Step S1: Test data (x1, x2, ..., x n ), calculate the mean and variance S 2 ;

[0009] Step S2: Generate a t-distributed random number with a degree of freedom of (n-1) and a chi-distributed random number with a degree of freedom of (n-1). 2 Distributed random numbers, denoted as t1, t2, ..., t l , z1, z2, …, z l , l≥100000;

[0010] Step S3: According to the mean Variance S 2 ,t i , z i , i = 1, 2, ..., l, calculate reliability;

[0011] Step S4: If the confidence is γ, take k = 1 (1-γ), then R (k) The corresponding value is the lower limit of reliability assessment.

[0012] Preferably, the mean value of step S1 and variance S 2 The calculation formula is:

[0013]

[0014] In the formula, χ i is the test data, and n is the number of test samples.

[0015] Preferably, the reliability calculation formula of step S3 is:

[0016]

[0017] Among them, U and L are the upper and lower limits of the performance parameters respectively. Get 1 R i The values ​​of and arrange them from small to large to obtain R (i) .

[0018] The technical effects of the reliability evaluation method of pyrotechnic output based on belief statistical inference of the present invention are as follows:

[0019] The present invention can use a small amount of test data to perform reliability evaluation when there is less test data and information on the output performance of the product being evaluated, and has the advantages of high evaluation accuracy and a small number of required samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0023] A reliability evaluation method for pyrotechnic output based on belief statistical inference includes the following steps:

[0024] Step S1: Test data (x1, x2, ..., x n ), calculate the mean and variance S 2 ;

[0025] Step S2: Generate a t-distributed random number with a degree of freedom of (n-1) and a chi-distributed random number with a degree of freedom of (n-1). 2 Distributed random numbers, denoted as t1, t2, ..., t l , z1, z2, ..., z l , l≥100000;

[0026] Step S3: According to the mean Variance S 2 ,t i , z i , i=1,2,…,l, calculate reliability;

[0027] Step S4: If the confidence is γ, take k = 1 (1-γ), then R (k) The corresponding value is the lower limit of reliability assessment.

[0028] The mean value of step S1 of this embodiment and variance S 2 The calculation formula is:

[0029]

[0030] In the formula, χ i is the test data, and n is the number of test samples.

[0031] The reliability calculation formula of step S3 of this embodiment is:

[0032]

[0033] Among them, U and L are the upper and lower limits of the performance parameters respectively. Get 1 R i The values ​​of and arrange them from small to large to obtain R (i) .

[0034] When this implementation plan is implemented,

[0035] When there is less output performance test data, faith inference can be used to evaluate the reliability of the output performance of pyrotechnics. Faith statistical inference does not rely on subjective prior information, but directly infers from the sample data itself. This makes the analysis results more objective and reduces the bias caused by improper prior selection. It is particularly suitable for situations where there is a lack of prior knowledge of the test data. Unlike some traditional frequentist methods based on point estimation or interval estimation, faith statistical inference can give a complete probability distribution about unknown parameters. We can not only know the possible range of values ​​of the parameters, but also understand the relative probability of each value. This provides richer information for decision-making, enabling decision makers to evaluate uncertainty more comprehensively. In the case of small samples, traditional frequentist methods may not be able to meet the conditions of asymptotic theory well, resulting in a decrease in the accuracy of estimation and inference. Faith statistical inference is relatively more advantageous when dealing with small sample data. It does not rely on the asymptotic properties of large samples, but infers through the careful use of sample information, and can provide more reliable results under limited data conditions.

[0036] Faith statistical inference has the advantages of requiring a small sample size, objective and stable results, and high accuracy.

[0037] The solution of the present application is further described below in conjunction with specific embodiments:

[0038] The output reliability of a certain detonator is evaluated. The upper and lower limits of the output performance parameters of this product are U = 22Mpa, L = 14Mpa, and the confidence level γ = 0.95. The output reliability of this product is analyzed.

[0039] The performance test data of the product is shown in Table 1.

[0040] Table 1 Performance test data

[0041] Serial number Product Number Pressure / (Mpa) 1 086 15.88 2 355 18.85 3 353 15.89 4 169 18.29 5 079 19.23 6 168 18.64 7 356 16.62 8 188 18.07 9 193 16.36 10 087 18.50

[0042] The reliability of the test piece is evaluated using the method proposed in this paper.

[0043] (1) Calculate the mean value based on the 10 output parameter test data and variance S 2 ;

[0044]

[0045] (2) Generate t-distributed random numbers with n-1 degrees of freedom and chi-distributed random numbers with n-1 degrees of freedom 2 Distributed random numbers, denoted as t1, t2, ..., t l, z1, z2, …, z l , l = 100000;

[0046] (3) S 2 ,t i , z i , i = 1, 2, ..., l, substitute into formula (1) to calculate the reliability:

[0047]

[0048] Among them, U and L are the upper and lower limits of the performance parameters respectively. Get 1 R i The values ​​of and arrange them from small to large to obtain R (i) ;

[0049] (4) k = l (1-γ) = 5000, R (5000) =0.9901, that is, the lower limit value of the reliability assessment corresponding to this product is 0.9901.

Claims

1. A reliability assessment method for pyrotechnic output based on belief statistical inference, characterized in that: The following steps are involved: Step S1: Test data (x1, x2, ..., x n ), calculate the mean and variance S 2 ; Step S2: Generate a t-distributed random number with a degree of freedom of (n-1) and a chi-distributed random number with a degree of freedom of (n-1). 2 Distributed random numbers, denoted as t1, t2, ..., t l ,z1,z2,...,z l , l≥100000; Step S3: According to the mean Variance S 2 ,t i , z i , i=1,2,...,l, calculate reliability; Step S4: If the confidence level is γ, take k = 1 (1-γ), then R (k) The corresponding value is the lower limit of reliability assessment.

2. The method for evaluating the reliability of initiator output based on belief statistical inference according to claim 1 is characterized in that: The mean value of step S1 and variance S 2 The calculation formula is: In the formula, χ i is the test data, and n is the number of test samples.

3. The method for evaluating the reliability of initiator output based on belief statistical inference according to claim 1 is characterized in that: The reliability calculation formula of step S3 is: Among them, U and L are the upper and lower limits of the performance parameters respectively. Get 1 R i The values ​​of and arrange them from small to large to obtain R (i) .

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