Unsupervised model parameter migration rolling bearing life prediction method

Abstract

The invention discloses an unsupervised model parameter migration rolling bearing life prediction method, and belongs to the technical field of rolling bearing state identification and residual life prediction. The method is provided for solving the problems that in practice, rolling bearing labeled vibration data under a certain working condition is difficult to obtain, health indexes are difficult to construct, and the service life prediction error is large. The method comprises the steps that firstly, extracting root-mean-square features from rolling bearing full-life-cycle vibration data,and introducing a new bottom-to-top time sequence segmentation algorithm to segment a feature sequence into three states composed of a normal period, a degradation period and a recession period; marking the state information of an amplitude sequence of the vibration signal subjected to fast Fourier transform, taking the amplitude sequence as an input of an improved full convolutional neural network, extracting deep features, and constructing a source-domain model and a state recognition model subjected to fine adjustment through training to realize rolling bearing multi-state recognition; andestablishing a rolling bearing life prediction model by using a state probability estimation method. Experiments prove that the method provided by the invention does not need to construct health indexes, can realize rolling bearing state identification and life prediction under different working conditions under an unsupervised condition, and obtains a better effect.

Description

technical field [0001] The invention specifically relates to a rolling bearing life prediction method based on unsupervised model parameter migration, and belongs to the technical field of rolling bearing state recognition and remaining life prediction. Background technique [0002] Rolling bearings are one of the vulnerable parts [1] , its remaining service life is closely related to the operating state of the equipment. It is of great significance to avoid downtime maintenance, casualties and other problems caused by bearing failure through the identification of bearing operating state and prediction of remaining life. [2-3] . [0003] At present, many scholars have conducted research on the state identification method of rolling bearings. Literature [4-5] respectively proposed the method of multi-objective optimization improved empirical mode decomposition, order statistical filter combined with fast singular spectrum decomposition to extract shallow fault features, whi...

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Problems solved by technology

[0011]Aiming at the difficulties in obtaining vibration data with labels for rolling bearings under certain working conditions in practice, the sample data in the target domain does not contain labels, it is difficult to construct health indicators, and the life prediction error is large. A Rolling Bearing Life Prediction Method Based on Unsupervised Model Parameter Migration

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