Rolling bearing performance degeneration evaluation method based on ADMM and sparse combination learning

A rolling bearing and combined learning technology, which is applied in mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., can solve the problem of time-consuming search for atoms

Inactive Publication Date: 2017-11-17
SOUTH CHINA UNIV OF TECH
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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 degeneration evaluation method based on ADMM and sparse combination learning
  • Rolling bearing performance degeneration evaluation method based on ADMM and sparse combination learning
  • Rolling bearing performance degeneration evaluation method based on ADMM and sparse combination 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 degeneration evaluation method based on ADMM and sparse combination learning. The method comprises steps that (1), a normal mode of a rolling bearing is learned, and the learned knowledge is utilized to establish a normal mode knowledge database; (2), a quantitative index solver based on the knowledge is established, a quantitative index phi of a detected roller bearing is solved; and (3), the quantitative index phi is analyzed, and rolling bearing performance degeneration degree evaluation is realized. The method is advantaged in that no prior knowledge is needed, Training Set characteristics can be learned to generate the accurate knowledge database; ADMM and combination learning is applied to the model, and model time complexity is made to realize reduction; the quantitative index for balancing a rolling bearing performance degeneration degree can be acquired, and practicality, quantitative evaluation and the high automation degree are realized.

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 Applications(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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