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Method for diagnosing and predicating rolling bearing based on grey support vector machine

A technology of support vector machines and rolling bearings, which is applied in mechanical bearing testing and other directions, can solve problems such as bearing failure prediction, bearing failure diagnosis and prediction, and achieve the effect of improving safe operation

Active Publication Date: 2015-05-06
BEIJING UNIV OF TECH +1
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Problems solved by technology

[0003] In order to solve the problem of bearing fault diagnosis and prediction, various algorithm models have been proposed, but these methods cannot effectively predict bearing faults

Method used

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  • Method for diagnosing and predicating rolling bearing based on grey support vector machine
  • Method for diagnosing and predicating rolling bearing based on grey support vector machine
  • Method for diagnosing and predicating rolling bearing based on grey support vector machine

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Experimental program
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Embodiment Construction

[0047] The implementation of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0048] Such as figure 1 As shown, 1. Rolling bearing failure experiment of Western Reserve University in the United States, the experimental platform includes a 2-horsepower motor (left), a torque sensor (middle), a power meter (right) and electronic control equipment (not shown) , the tested bearing supports the motor shaft. The bearing model is SKF bearing, and single-point faults are arranged on the bearings using EDM technology, and the fault diameters are 0.007, 0.014, and 0.028 inches respectively. In the experiment, an acceleration sensor is used to collect vibration signals, and the sensors are respectively installed on the drive end and fan end of the motor housing and the motor support chassis. The vibration signal is collected by a 16-channel DAT recorder, and the sampling frequency of the digital signal is 12000S / s.

[0049]The...

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Abstract

The invention provides a method for diagnosing and predicating a rolling bearing based on a grey support vector machine. The method is characterized in that the rolling bearing is used as a key part of a mechanical device, and the advantages and disadvantages of the operation state influence the operation performances of the whole device. The method is the method for diagnosing and predicating the rolling bearing based on GM (1, 1)-SVM. The method comprises the steps of extracting a vibration signal time domain and frequency domain feature values of the rolling bearing under various fault and normal states; selecting important feature parameters to build a predicating model, namely, grey model; predicating the feature value; training a binary tree supporting vector machine according to various fault feature values and normal state feature values of the bearing; creating a rolling bearing decision making tree for determining the fault as well as classifying the fault type to diagnosis the fault of the bearing; then predicating the fault according to the predicating value and the trained supporting vector machine.

Description

technical field [0001] The invention belongs to the field of bearing fault diagnosis, and is a comprehensive fault diagnosis and prediction model GM(1,1)-SVM developed for rolling bearings. Background technique [0002] Rolling bearings are the most widely used mechanical parts in electric power, petrochemical, metallurgy, machinery, aerospace and some military industries, and are also one of the most vulnerable parts. It has the advantages of high efficiency, small frictional resistance, convenient assembly, and easy lubrication. It is widely used in rotating machinery and plays a key role. Many failures of rotating machinery are closely related to rolling bearings. According to relevant statistics, 70% of mechanical failures are vibration failures, and 30% of vibration failures are caused by rolling bearings. The consequences caused by rolling bearing failure range from reduced and lost some functions of the system to severe or even catastrophic consequences. So the fau...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
Inventor 高亚举杨建武亢太体刘志峰王建华
Owner BEIJING UNIV OF TECH
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