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, the test data is collected, then the performance index degradation model is established, and the unknown parameters in the model are estimated, and finally the life and reliability prediction is carried out. The specific steps are: step 1: collect test data; step 2: establish a degradation model; step 3: estimate parameters and update the model in real time; step 4: predict life and reliability. For the phenomenon that each index of multi-performance index products shows "clustering" due to the influence of common potential factors, mathematical modeling is carried out, which makes the degradation modeling of products with this phenomenon more realistic, and the difference between indexes is more realistic. The origin of the correlation is better explanatory.

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 with high reliability and long 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 methods become difficult to adapt to real-world 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 degradati...

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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