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Aerospace product reliability evaluation method based on multi-stage test data conversion
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A technology of test data and reliability, applied in electrical digital data processing, instruments, geometric CAD, etc., can solve problems such as unsatisfactory evaluation accuracy
Active Publication Date: 2020-09-01
NAT UNIV OF DEFENSE TECH
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Classical analysis and calculation methods, such as modified maximum likelihood function method, sample space sorting method, etc., can perform reliability evaluation without prior information, so their evaluation accuracy may not be satisfactory
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Embodiment 1
[0089] refer to figure 1 , providing a reliability evaluation method for aerospace products based on multi-stage test data conversion, including the following steps:
[0090] Step 1: Carry out two-stage reliability tests of aerospace products, and obtain product test life data of the first and second stages of aerospace products:
[0091] In the first phase reliability test of aerospace products: there are n 1 samples into the experiment, at the cut-off time t a When there are r failures, the failure data is t 1 , t 2 ,...,t r , no failure data is t r+1 ,t r+2 ,...,t n1 , where t r+1 = t r+2 =...=t n1 = t a , the Weibull life distribution parameter at this stage is (m 1 , η 1 ).
[0092] In the second phase reliability test of aerospace products: there are n 2 samples into the experiment, at the cut-off time t b When no failure data is generated, the Weibull life distribution parameter at this stage is (m 2 , η 2 ).
[0093] The second step is to convert the...
Embodiment 2
[0155] In this embodiment, according to a certain unit project, the Weibull distribution parameter of the rocket engine bearing in the first stage is m 1 =1.5, η 0 =2000, and 10 products are put into test (n 1 =n 2 =10), the test censored time t a = t b =500s, rated task working time τ=500s. Simulation based on this parameter to get a failure data t during the test 1 =316s test data.
[0156] The aerospace product reliability evaluation method based on multi-stage test data conversion provided by the present invention is used to estimate the reliability, and the results are as follows:
[0157] The reliability evaluation result of the first stage is: m 1 =1.5, η 1 = 2243.1.
[0158] The calculation result of the conversion factor is: C 1 = 0.8431, C 2 =…=C n = 1.6357.
[0159] The experimental data after conversion is: t 11 =266.39,t 12 =...=t 1n = t 1a = 817.84.
[0160] The result of the prior distribution of the second stage is:
[0161]
[0162]
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Abstract
The invention belongs to the field of reliability evaluation, and particularly relates to an aerospace product reliability evaluation method based on multi-stage test data conversion, which comprisesthe following steps: carrying out a two-stage reliability test on an aerospace product to obtain product test life data of a first stage and a second stage of the aerospace product; converting the product test life data of the first stage; determining pre-test distribution of Weibull life distribution parameters in the second stage, and obtaining a Bayes post-test distribution kernel function under the joint distribution data; and carrying out reliability interval estimation by adopting an M-H sampling method based on Bayes. The invention provides an inter-stage data conversion method based onWeibull distribution. Bayes joint posteriori distribution is obtained by combining the converted data and new data formed by the data in the current stage, then estimated values of parameters are obtained by adopting a Metropol-Hastings (M-H) algorithm, reliability evaluation is completed, and the product reliability evaluation precision is improved.
Description
technical field [0001] The invention belongs to the field of reliability evaluation, and in particular relates to a method for evaluating the reliability of aerospace products with no failure data in multi-stage tests based on a conversion factor method. Background technique [0002] Aerospace products such as rocket engine turbo pumps and bearings have the characteristics of high reliability and small samples, and are also limited by time and budget. Therefore, very few failures or even no failures may occur during the reliability test. In addition, in engineering practice, the product design is usually improved after a stage of testing is completed to make up for product defects and improve product reliability. Therefore, products between different stages belong to different life distribution matrices, and the parameters of the life distribution change. [0003] Since the Weibull distribution was proposed in the middle of the 20th century, its application has been widely ...
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