No-failure data ultra-small sample-based product life distribution assessment method

A technology with no failure data and product life, which is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of low precision of two-parameter Weibull distribution and the inability to accurately express the real situation of product life distribution, etc.

Inactive Publication Date: 2018-09-04
NORTHEASTERN UNIV
View PDF5 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Aiming at the shortcomings of the product life distribution evaluation in the prior art that the expression accuracy of the two-parameter Weibull distribution is not high, and the real situation of the product life di

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • No-failure data ultra-small sample-based product life distribution assessment method
  • No-failure data ultra-small sample-based product life distribution assessment method
  • No-failure data ultra-small sample-based product life distribution assessment method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The present invention will be further elaborated below in conjunction with the accompanying drawings of the description.

[0034] A product life distribution evaluation method based on an ultra-small sample of non-failure data of the present invention comprises the following steps:

[0035] 1) The shape parameter is determined, assuming that the life of the product to be estimated obeys the Weibull distribution and the shape parameter β is 1.5 to 2.5;

[0036] 2) Position parameter estimation. If there are n life samples, they are arranged according to the life sample values ​​from small to large. Among them, the life of the i-th sample is ti, and the median rank formula for estimating the probability of success is

[0037]

[0038] In the formula, i is the number of unfailed samples in all n samples, F 2(n+1-i),2i,0.5 is the median of 2(n+1-i) and 2i F distributions with degrees of freedom;

[0039]

[0040] 3) Scale parameter estimation, for success / failure st...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a no-failure data ultra-small sample-based product life distribution assessment method. The method comprises the following steps of: estimating a shape parameter (as shown inthe specification) of product life which obeys Weibull distribution, wherein the shape parameter is selected from 1.5 to 2.5; estimating a position parameter, obtaining a median rank of a parameter estimation success probability according to a life sample value, and obtaining a relationship between a sample amount under a stipulated confidence level and the position parameter; estimating a scale parameter, and expressing a relationship (as shown in the specification) among reliability, the confidence level, the sample amount and a failure amount by using binomial distribution, wherein R is thereliability, C is the confidence level, j is a failure sample number, an equation (as shown in the specification) exists to obtain a scale parameter estimation formula (as shown in the specification)under the given confidence level. Through the method, life distribution, a confidence coefficient of which is 95% is estimated by using a unilateral interval estimation method according to an empirical value of the product life Weibull distribution shape parameter and the estimated position parameter, so that effectiveness of the method is verified.

Description

technical field [0001] The invention relates to a product life distribution evaluation method, in particular to a product life distribution evaluation method based on an ultra-small sample without failure data. Background technique [0002] The situation of no failure data means that no sample fails within the specified test time (so there is no exact life data), which is a special case of timed censored test in reliability test. In the early days, non-failure data was often removed as abnormal data, or simply treated conservatively. For long-life and high-reliability products, it will take quite a long test time to obtain their failure data. With the increasing number of high-reliability and long-life products, there will be more and more cases of no failure data in time-limited timed censored tests. Therefore, how to statistically process the test results without failure data, especially when the sample size is small, has attracted increasing attention from many scholars...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 谢里阳樊富友吴宁祥李海洋
Owner NORTHEASTERN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap