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Fault diagnosis method for a rotating mechanical ball bearing based on K-means clustering and evidence reasoning

A k-means clustering, ball bearing technology, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as dangerous accidents, cascading damage, affecting the normal operation of other components in the equipment, etc.

Active Publication Date: 2019-09-03
HANGZHOU DIANZI UNIV
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Problems solved by technology

The failure of ball bearings will affect the normal operation of other components in the equipment, which will trigger a series of chain damage reactions and even cause more dangerous accidents

Method used

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  • Fault diagnosis method for a rotating mechanical ball bearing based on K-means clustering and evidence reasoning
  • Fault diagnosis method for a rotating mechanical ball bearing based on K-means clustering and evidence reasoning
  • Fault diagnosis method for a rotating mechanical ball bearing based on K-means clustering and evidence reasoning

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Embodiment Construction

[0043] A kind of rotating machinery ball bearing fault diagnosis method based on K-means clustering and evidence reasoning proposed by the present invention, its flow chart is as follows figure 1 shown, including the following steps:

[0044] (1) Set the fault set Θ={F of the rotating machinery ball bearing 1 ,...,F i ,...,F N |i=1,2,...,N}, F i Indicates the i-th fault in the fault set Θ, and N is the number of fault modes contained in the ball bearing.

[0045] (2) Let f 1,i , f 2,i and f 3,i In order to be able to reflect each fault F in the fault set Θ i The characteristic parameter of the fault is the acceleration signal, which is provided by the acceleration sensor installed at the drive end of the motor case, the fan end at the 12 o'clock position, and the motor base, respectively. 1,i (t), f 2,i (t), f 3,i (t) and F i Expressed as a sample set M i ={[f 1,i (t), f 2,i (t), f 3,i (t), F i ]|t=1,2,...,S i}, where [f 1,i (t), f 2,i (t), f 3,i (t), F i ...

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Abstract

The invention relates to a fault diagnosis method for a rotating mechanical ball bearing based on K-means clustering and evidence reasoning. According to the method, a likelihood reliability table andK reference center vectors are obtained by K-means clustering and K reference evidences are obtained by the likelihood reliability table; distances between fault feature data and the K reference center vectors are calculated, the reference evidences are corrected, and K diagnostic evidences are generated; and after on-line acquisition of values of multiple kinds of fault characteristics, activated diagnostic evidences are calculated respectively, the activated diagnostic evidences are fused, a fault decision is made by using the fused evidences, and a fault type corresponding to the online fault characteristic data is obtained. According to the invention, the fusion reasoning of fault diagnosis evidences is performed based on K-means clustering; and with the multi-source diagnostic information, the fault diagnosis accuracy of the rotating mechanical ball bearing can be improved effectively.

Description

technical field [0001] The invention relates to a fault diagnosis method for a rotating machinery ball bearing based on K-means clustering and evidence reasoning, and belongs to the technical field of state monitoring and fault diagnosis of the rotating machinery ball bearing. Background technique [0002] Ball bearings are widely used in rotating machinery due to the advantages of small frictional resistance, simple structure, low price, and good lubrication performance. As one of the tiny parts of mechanical equipment, it has the functions of bearing load, transmitting power and torque, and plays a pivotal role in mechanical equipment, especially the core component of rotating machinery system. As a "joint" connecting rotating parts and fixed parts in mechanical equipment, ball bearings are subject to various alternating loads during operation, and their operating status will inevitably change over time. In addition, due to machining errors, improper installation or opera...

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Application Information

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IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 徐晓滨张雪林杨颖胡燕祝李建宁黄大荣韩德强
Owner HANGZHOU DIANZI UNIV
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