Degradation modeling and life prediction method considering performance index clustering in dynamic environment

Abstract

The invention relates to a degradation modeling and life prediction method considering performance index clustering in a dynamic environment, and belongs to the technical field of degradation modeling and life prediction. Firstly, test data are collected, then a performance index degradation model is established, unknown parameters in the model are estimated, and finally life and reliability prediction is carried out. The method comprises the following specific steps: 1, collecting test data; 2, establishing a degradation model; 3, estimating parameters, and updating the model in real time; and 4, predicting the service life and the reliability. For the phenomenon that each index of a multi-performance index product shows a clustering phenomenon due to the influence of public potential factors, mathematical modeling is carried out, so that degradation modeling for the product with the phenomenon is more practical, and a better explanatory is provided for the origin of correlation among the indexes.

Description

technical field [0001] The invention relates to a degradation modeling and life prediction method considering performance index clustering in a dynamic environment, and belongs to the technical field of degradation modeling and life prediction. Background technique [0002] With the development of science and technology, many products are designed to have higher reliability and longer life, which makes it difficult to conduct life tests and obtain product failure data. Therefore, traditional reliability modeling and life prediction based on failure data The method becomes difficult to adapt to the real needs. The improvement of sensor technology and the rise of fault prediction and health management technology have made it possible to obtain a large amount of data related to product performance degradation, and product degradation modeling and life prediction technology based on degradation data have also received extensive attention and research. [0003] At present, most ...

AI Technical Summary

Problems solved by technology

The use of multidimensional distribution to describe the degradation process of product performance indicators has the following limitations: (1) All performance indicators are required to meet the same degradation law, that is, the individual degradation models of each indicator are consistent; (2) The degradation amount or degradation increase of each indicator is required Quantities follow the same distribution

[0010] Although the Copula function can handle complex correlations more flexibly, there are still many shortcomings: (1) How to choose an appropriate Copula function is still a difficult problem; (2) Most of the Copula functions are complex in form and difficult to operate in practice

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