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Rolling bearing life stage identification method based on MAMTL

A rolling bearing and identification method technology, applied in the field of rolling bearing life prediction, can solve the problems of low recognition accuracy of life stage samples and large differences in sample distribution, and achieve improved generalization performance, good generalization and domain adaptation sexual effect

Pending Publication Date: 2022-03-18
SICHUAN UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the life stage identification accuracy rate caused by the large difference in sample distribution, the small number of training life stage samples, and the uneven number of samples in different life stages in the identification of rolling bearing life stages under variable working conditions in the prior art. The lower problem is to provide a MAMTL-based rolling bearing life that can use a small number of non-equal life stage samples (ie, training samples with class labels) under the historical working conditions of rolling bearings to identify the life stage of the current sample to be tested with high precision. stage identification method

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  • Rolling bearing life stage identification method based on MAMTL
  • Rolling bearing life stage identification method based on MAMTL
  • Rolling bearing life stage identification method based on MAMTL

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Embodiment Construction

[0075] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0076] The MAMTL of the present invention represents Model-Agnostic Meta-Transfer Learning (MAMTL). In MAMTL, model-independent meta-learning and transfer learning are combined to achieve multi-task synchronous parallel training to replace traditional iterative training. Multiple task loss functions use unlabeled samples under different working conditions and a small number of valid training under historical working conditions. The class label samples jointly update the MAMTL network parameters to seek the global optimal solution of the network parameters, which makes MAMTL have better generalization ability, so MAMTL has better performance than traditional transfer learning in the case of fewer historical working conditions with class label training samples. Good domain adaptability; a new type of prototype network is built in MAMTL to repres...

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Abstract

The invention discloses a rolling bearing life stage identification method based on MAMTL, and the method comprises the following steps: S1, carrying out the life stage division of the full life data of a rolling bearing, and dividing the full life data into four stages: a normal stage, an early degradation stage, a medium degradation stage, and a complete failure stage; s2, collecting the vibration acceleration of the rolling bearing of which the life stage division is completed in the whole life stage as a source domain sample set, and collecting the vibration acceleration of the rolling bearing to be identified as a target domain sample set; s3, training an MAMTL network, wherein the MAMTL is composed of an inner ring parallel network, an outer ring element learning network and a prototype network; and S4, identifying class labels of to-be-tested samples of the target domain: completing classification of the to-be-tested samples of the target domain by using the trained MAMTL, namely completing life stage identification of the rolling bearing. According to the method, a small number of non-equal life stage samples under the historical working condition of the rolling bearing can be used for carrying out high-precision life stage identification on the to-be-detected sample under the current working condition.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing life prediction, in particular to a method for identifying life stages of rolling bearings based on MAMTL. Background technique [0002] Rolling bearings are widely used in various key equipment such as gas turbines, aero engines, and wind turbines. Their life and reliability largely determine whether the equipment can operate normally, achieve predetermined functions, and achieve expected service life. Rolling bearings will go through a series of different life stages throughout their service. Therefore, the research on the life stage identification of rolling bearings is helpful to avoid catastrophic accidents caused by key equipment failures, reduce equipment maintenance costs, and improve equipment efficiency. Candidate rolling bearings are screened out and the bearings with the optimal life are installed in the equipment. [0003] At present, the research on the life stage identific...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214G06F18/2415
Inventor 李锋李统一汪永超
Owner SICHUAN UNIV
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