Adaptive sparse graph learning bearing life prediction method based on digital twin dictionary

Abstract

The invention discloses an adaptive sparse graph learning bearing life prediction method based on a digital twin dictionary, and the method comprises the steps: building an expansion index model and a linear segmentation model, generating a digital twin dictionary covering a plurality of degeneration behaviors, designing a new graph learning optimization objective function, and carrying out the prediction of the life of a bearing. A sparse regularization method is introduced to reduce model complexity and parameter sensitivity, an accurate topological structure of data is adaptively acquired, and accurate prediction of the remaining service life is realized based on a constructed digital twin dictionary and adaptive sparse graph learning.

Description

technical field [0001] The invention belongs to the technical fields of mechanical failure prediction and health management, and signal processing, and relates to a bearing life prediction method based on a digital twin dictionary based on adaptive sparse graph learning. Background technique [0002] The support and transmission parts of mechanical equipment such as bearings, gears, shafts, etc. are key components. Once the part fails, the mechanical equipment will not work normally, and serious safety accidents will occur, which will bring huge losses to production and life. If the fault can be detected as early as possible, and even the occurrence of the fault can be predicted, it will be more valuable in practice. Since rolling bearings are widely used in rotating machinery and are extremely prone to failure, research on the prediction of the remaining service life of rolling bearings has received more and more attention. [0003] The current remaining service life pred...

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Problems solved by technology

[0005] The purpose of the present invention is to provide an adaptive sparse graph learning bearing life prediction method based on the digital twin dictionary to solve the problems existing in the rolling bearing life prediction

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