Bearing fault diagnosis method based on transfer learning

A technology of transfer learning and fault diagnosis, which is applied in the direction of mechanical bearing testing, etc.

Active Publication Date: 2016-02-03
SOUTHEAST UNIV
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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing machine learning bearing fault diagnosi

Method used

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  • Bearing fault diagnosis method based on transfer learning
  • Bearing fault diagnosis method based on transfer learning
  • Bearing fault diagnosis method based on transfer learning

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Embodiment Construction

[0058] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0059] see figure 1 , a bearing fault diagnosis method based on transfer learning of the present invention comprises the following steps:

[0060] Step A: Use autocorrelation singular value decomposition (SVD) for the target data and auxiliary data to extract the contained fault singular value vectors (ie, fault feature vectors), and perform sample collection according to the selection rules of the training data set T and the test data set S select;

[0061] In step A, the target data source is: the vibration data of the target bearing object in the target environment, and the auxiliary data source is: the vibration data of the non-target environment or non-target object; the target data and auxiliary data of the bearing system are vibration acceleration signals, and the bearing An example of a vibration signal is shown in figure 2 shown;...

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Abstract

The invention discloses a bearing fault diagnosis method based on transfer learning. The method comprises the steps of selection rules of a training data set and a test data set; auxiliary data normalization transferable degree quantification and a threshold selection method; and the algorithm flow of a TrAdaboost algorithm based on weight adjustment used for bearing fault classification. A thinking strategy that auxiliary data in transfer learning assist target data learning is introduced by aiming at problems of lacking of diagnosis generality caused by the conditions of variable working conditions, unavailable direct measurement and new fault types. According to the method, diagnosis precision of conventional machine learning in a small amount of target data can be enhanced, environmental adaptability of bearing fault diagnosis is enhanced, and the method has potential economic value in the field of bearing fault application under variable working condition loads, new faults and indirect measurement.

Description

technical field [0001] The invention relates to a mechanical fault diagnosis method, in particular to a bearing fault diagnosis method based on migration learning. Background technique [0002] Most of the current bearing fault diagnosis is based on the assumption that the training data set and the test data set have the same feature space and data distribution, and most of the models are based on the laboratory environment or ideal environment, ignoring the actual situation of the mechanical system when it is running. Conditions such as variable working conditions, inability to measure directly, and new fault types often lead to less or even unobtainable target fault data in bearing fault diagnosis, and lead to different distribution characteristics of training fault data and target fault data. The traditional machine learning method is based on the premise that the training data and the test data have the same characteristics and the amount of data is sufficient, so it is ...

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Application Information

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IPC IPC(8): G01M13/04
Inventor 严如强沈飞
Owner SOUTHEAST UNIV
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