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Fault diagnosis method of rolling bearing under variable working conditions based on feature transfer learning

A rolling bearing and fault diagnosis technology, which is applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., can solve the problems of low diagnostic accuracy and the inability to obtain a large amount of tagged vibration data for rolling bearings

Active Publication Date: 2019-07-19
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0006] The invention aims at the problem that it is difficult or impossible to obtain a large amount of labeled vibration data for rolling bearings, especially under variable working conditions, resulting in low diagnosis accuracy, and further provides a rolling bearing fault diagnosis method under variable working conditions based on feature transfer learning

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  • Fault diagnosis method of rolling bearing under variable working conditions based on feature transfer learning
  • Fault diagnosis method of rolling bearing under variable working conditions based on feature transfer learning
  • Fault diagnosis method of rolling bearing under variable working conditions based on feature transfer learning

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Embodiment Construction

[0081] Such as Figures 1 to 7 As shown, the realization process of the rolling bearing fault diagnosis method under variable working conditions based on feature transfer learning (based on feature multi-core SSTCA-SVM rolling bearing fault diagnosis method under variable working conditions) is given in this embodiment as follows:

[0082] (1) Feature extraction:

[0083] Perform VMD operation on the vibration signals of rolling bearings with known and unknown working conditions (normal under different speeds and different load conditions, different failure degrees of the inner ring, different failure degrees of the outer ring, and different failure degrees of the rolling elements), and use observation Determine the number of IMFs to be decomposed, build a matrix for the IMF, and perform SVD to obtain singular values, and at the same time obtain the singular value entropy; then extract the time domain and frequency domain characteristic indicators of the vibration signal;

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Abstract

The present invention provides a rolling bearing fault diagnosis method in various working conditions based on feature transfer learning, and relates to the field of fault diagnosis. The objective ofthe invention is to solve the problem that a rolling bearing, especially to various working conditions, is low in accuracy of diagnosis. The method comprise the steps of: employing a VMD (VariationalMode Decomposition) to perform decomposition of vibration signals of a rolling bearing in each state to obtain a series of intrinsic mode functions, performing singular value decomposition of a matrixformed by the intrinsic mode functions to solve a singular value or a singular value entropy, combining time domain features and frequency domain features of the vibration signals to construct a multi-feature set; introducing a semisupervised transfer component analysis method to perform multinuclear construction of a kernel function thereof, sample features of different working conditions are commonly mapped to a shared reproducing kernel Hilbert space so as to improve the data intra-class compactness and the inter-class differentiation; and employing the maximum mean discrepancy embedding to select more efficient data as a source domain, inputting source domain feature samples into a SVM (Support Vector Machine) for training, and testing target domain feature samples after mapping. Therolling bearing fault diagnosis method in various working conditions has higher accuracy in the rolling bearing multi-state classification in various working conditions.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method under variable working conditions, and relates to the technical field of fault diagnosis. Background technique [0002] Rolling bearings are one of the key components of large-scale rotating machinery, and fault diagnosis is helpful to prevent equipment accidents [1] . When rolling bearings are actually working, the working conditions often change. In recent years, research on fault diagnosis of rolling bearings under variable working conditions has attracted widespread attention from scholars. [0003] Literature [2] combined Hilbert-Huang transform and Singular value decomposition (Singular value decomposition, SVD) to extract the characteristics of rolling bearing vibration signals, and then used recurrent neural network to realize bearing fault classification under variable working conditions; Literature [3] proposed a A method combining local mean decomposition and SVD can effect...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 王玉静康守强胡明武谢金宝王庆岩邹佳悦
Owner HARBIN UNIV OF SCI & TECH
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