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Bearing fault diagnosis method based on LS-SVM and D-S evidence theory

A technology of LS-SVM and evidence theory, applied in the field of bearing fault diagnosis based on LS-SVM and D-S evidence theory, can solve the problem of different measurement signals

Inactive Publication Date: 2018-07-27
CHINA RAILWAYS CORPORATION +1
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Problems solved by technology

However, due to the difference in accuracy of different sensors and the random error in the measurement, the actual measurement signal may be far different from the expected

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  • Bearing fault diagnosis method based on LS-SVM and D-S evidence theory
  • Bearing fault diagnosis method based on LS-SVM and D-S evidence theory
  • Bearing fault diagnosis method based on LS-SVM and D-S evidence theory

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Embodiment Construction

[0032] The present invention is described in detail by the following examples. It is necessary to point out that this example is only used to further illustrate the present invention, and can not be interpreted as limiting the protection scope of the present invention. Those skilled in the art can according to the above invention Some non-essential improvements and adjustments have been made to the content. In the case of no conflict, the embodiments and the features in the embodiments of the present invention can be combined with each other.

[0033] 1. Least squares support vector machine (LS-SVM):

[0034] Support vector machines (SVMs) are a machine learning method developed by Vapnik and colleagues based on statistical learning theory and structural risk minimization principles. It has good generalization ability and can always find the global optimal solution during training, so it has been widely used in the field of empirical modeling. However, the training of SVM is...

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Abstract

The invention discloses a bearing fault diagnosis method based on an LS-SVM and the D-S evidence theory. Through a multi-level information fusion method, the least squares support vector machine (LS-SVM) is adopted in a feature layer, and the D-S evidence theory is adopted in a decision-making layer to solve the problems that the single sensor fault diagnosis accuracy is low and the sensitive features are difficult to extract. Firstly, the wavelet noise reduction technology is used to improve the signal-to-noise ratio of a rolling bearing vibration signal, and eight parameters in the time domain and the frequency domain are introduced as feature parameters of the bearing vibration. Secondly, the bearing is fault identified by the LS-SVM. Thirdly, the LS-SVM feature output is used as the D-S evidence theory input, and the D-S evidence theory is used for fault decision making. The method can effectively improve the accuracy of rolling bearing fault diagnosis. The invention has certain significance for improving the fault diagnosis accuracy of the rolling bearing and the reliability of the diagnostic system.

Description

technical field [0001] The invention relates to the technical field of fault identification, in particular to a bearing fault diagnosis method based on LS-SVM and D-S evidence theory. Background technique [0002] As an important part of rotating machinery, rolling bearings are widely used in industry, agriculture, transportation and other industries, and their operating status is directly related to the safety and efficiency of mechanical equipment. Under harsh working conditions, due to the influence of load, installation, lubrication and other factors, rolling bearings will have various types of failures after a period of operation. Therefore, the rolling bearing is a relatively weak link in the rotating machinery, and it is of great significance to study the fault diagnosis method of the rolling bearing. [0003] In recent years, many methods have been used for bearing fault detection, such as vibration signal detection, oil analysis detection, temperature detection, ac...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04G06K9/62
CPCG01M13/045G06F18/2411
Inventor 岳建海单巍杨国栋杨江天焦静徐占山吴裕源沈泓周航
Owner CHINA RAILWAYS CORPORATION
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