A method and device for bearing fault diagnosis based on supervised lle algorithm

A fault diagnosis and supervised technology, applied in measurement devices, complex mathematical operations, testing of mechanical components, etc., to achieve the effect of improving the online prediction rate

Active Publication Date: 2021-03-26
FOSHAN UNIVERSITY
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Problems solved by technology

[0003] However, in the current field of bearing fault diagnosis, there are often large-scale concurrent data, which brings great challenges to the real-time requirements of fault diagnosis, and it is urgent to improve the online prediction rate of bearing fault diagnosis

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  • A method and device for bearing fault diagnosis based on supervised lle algorithm
  • A method and device for bearing fault diagnosis based on supervised lle algorithm
  • A method and device for bearing fault diagnosis based on supervised lle algorithm

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[0052] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0053] refer to figure 1 , a bearing fault diagnosis method based on a supervised LLE algorithm provided by an embodiment of the present invention includes the following steps:

[0054] Step S100, acquiring training data, the training data is historical data representing the vibration signal of the bearing, extracting the eigenvalues ​​of the training data and the fault types corresponding to the eigenvalues;

[0055] Step S200, determine the preferred dimensionality reduction training data of the training da...

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Abstract

A bearing fault diagnosis method and apparatus based on a supervised LLE algorithm, the method comprising: acquiring training data, the training data being historical data representing bearing vibration signals, and extracting feature values of the training data and fault types corresponding to the feature values (S100); determining optimal dimensionality reduction training data of the training data and calculating the mean value and covariance matrix corresponding to each fault type in the optimal dimensionality reduction training data (S300); performing dimensionality reduction on test data received in real time to obtain dimensionality reduction test data (S400); and, on the basis of the mean values and the covariance matrices, calculating the probability value of the dimensionality reduction data in each fault type, and using the fault type with the greatest probability value as the fault type for the bearing fault diagnosis (S500). Thus, the online prediction rate of bearing fault diagnosis is improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a bearing fault diagnosis method and device based on a supervised LLE algorithm. Background technique [0002] As an emerging comprehensive marginal subject, bearing fault diagnosis technology has initially formed a relatively complete subject system. As far as its technical means are concerned, vibration diagnosis technology has become the mainstream technology of bearing fault diagnosis. The rapid progress of computer technology and signal information processing technology has greatly promoted the development of bearing fault diagnosis and monitoring technology in a scientific and practical direction. [0003] However, in the current field of bearing fault diagnosis, there are often large-scale concurrent data, which brings great challenges to the real-time requirements of fault diagnosis, and it is urgent to improve the online prediction rate of bearing fault diagnosi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045G06F17/16
CPCG01M13/045G06F17/16
Inventor 张彩霞曾平王向东
Owner FOSHAN UNIVERSITY
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