Rolling bearing performance degradation evaluation method based on admm and sparse combined learning

A rolling bearing and combined learning technology, which can be used in mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., and can solve problems such as time-consuming search for atoms

Inactive Publication Date: 2019-05-14
SOUTH CHINA UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

During the test phase, the process of searching atoms from a huge dictionary is very time-consuming

Method used

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  • Rolling bearing performance degradation evaluation method based on admm and sparse combined learning
  • Rolling bearing performance degradation evaluation method based on admm and sparse combined learning
  • Rolling bearing performance degradation evaluation method based on admm and sparse combined learning

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Embodiment

[0065] The data used in this embodiment comes from the National Aeronautics and Space Administration (NASA) pre-diagnosis database, provided by the research group of Professor Jay Lee of the Intelligent Maintenance System Center (IMS) of the University of Cincinnati.

[0066] In this embodiment, we only use the vibration data of bearing 3 of experiment No.1 in the IMS database. At the beginning of the experiment, it was assumed that the performance of the rolling bearing was good. We assume that the data of the first ten days of the experiment were collected when the bearing performance was good (note: the experiment time was from 2003-10-22 to 2003-10-31, a total of 368 sets of data), and the data of the first ten days were normalized One-time preprocessing, and then form the Training Set of the first iteration. The rest of the data were normalized and preprocessed according to the Training Set method, and then formed the Test Set (Note: The experiment time was from 2003-11-...

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Abstract

The invention discloses a rolling bearing performance degradation evaluation method based on ADMM and sparse combined learning, the method includes the following steps: (1) learning the normal mode of the rolling bearing, and using the learned knowledge to establish a knowledge base of the normal mode; 2) Establish a quantitative index solver based on various knowledge to solve the quantitative index Φ of the tested rolling bearing; (3) Analyze the quantitative index Φ to realize the evaluation of the degradation degree of rolling bearing performance. This method does not require prior knowledge, and can generate a correct knowledge base by learning the characteristics of the Training Set; applying ADMM and combination learning to the model reduces the time complexity of the model; and can obtain a quantitative index Φ to measure the degradation degree of rolling bearing performance , has the advantages of real-time, quantitative evaluation and high degree of automation.

Description

technical field [0001] The invention relates to the field of performance degradation evaluation of rolling bearings, in particular to a performance degradation evaluation method of rolling bearings based on ADMM and sparse combination learning. Background technique [0002] In recent decades, the entire machine tool has been paralyzed due to rolling bearing failures, resulting in economic losses, and even more casualties have occurred from time to time. According to statistics, in mechanical equipment containing rolling bearings, about 30% of mechanical failures are related to bearing degradation. [0003] How to accurately estimate the performance degradation degree of rolling bearings in real time has always been a hot issue concerned by academic and engineering circles at home and abroad. The existing rolling bearing performance degradation evaluation techniques can be divided into three categories: time domain analysis, frequency domain analysis and time-frequency domai...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/04G06K9/62
CPCG01M13/04G01M13/045G06F18/2136
Inventor 罗飞周裕华
Owner SOUTH CHINA UNIV OF TECH
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