Poor information theory fusion-based product life characteristic information extraction method

Abstract

The invention relates to a poor information theory fusion-based product life characteristic information extraction method, which comprises the following steps of: acquiring original information of a small sample; transforming the original information into large-sample generating information by using a right self-service method and performing effective maximum likelihood processing, and acquiring maximum likelihood estimated values of large-sample content of two parameters, namely a Weibull distribution shape parameter and a scale parameter; extracting density functions of the two parameters by using a maximum entropy method; giving a confidence level, and calculating estimation intervals and expected values of the two parameters through the density functions of the shape parameter and the scale parameter respectively; and giving a failure probability, and acquiring the product life characteristic information through Weibull distribution life of the two parameters and reliability calculation thereof. The method has no requirement on completeness of the original information of the small sample, does not need priori information of the shape parameter and the scale parameter, can effectively recover total original characteristics of the product life, disclose nature of the product life information, more accurately acquire the product life characteristic information and reduce the experimental quantity of the product.

Description

technical field [0001] The invention relates to a method for extracting product life feature information, in particular to a method for extracting life feature information of rolling bearings based on fusion of lack of information theory, belonging to the technical field of product life and its reliability evaluation and prediction. Background technique [0002] Many products, such as rolling bearings, especially aerospace bearings, nuclear reactor bearings and wind power bearings, not only require life and reliability evaluation, but also require confidence interval evaluation of reliability. This is a new indicator for product accuracy life and performance life research. Theoretically, this requirement is reasonable, because according to uncertainty theory, the estimated value of any parameter has uncertainty; in addition, metrology also requires that the estimated value of any parameter should be accompanied by its confidence level and confidence interval. The calculated...

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Problems solved by technology

Therefore, the bootstrap maximum entropy method cannot solve the small-sample evaluation problem with less than 2 information samples, nor can it solve the small-sample evaluation problem with little original information

[0010] In particular, both the maximum entropy bootstrap method and the bootstrap maximum entropy method use bootstrap sampling, which cannot solve the evaluation problem with different attribute information, so parameter estimation, parameter density function estimation, parameter density function estimation, and Product Life and Reliability and Estimation of Confidence Interval

[0011] In summary, for small-sample timed censored experiments, it is still a difficult problem how to evaluate the confidence interval of life and its reliability without any prior information on Weibull distribution parameters

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