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Rolling bearing fault prediction method based on wavelet principal component analysis

A rolling bearing and component analysis technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as high time complexity of algorithms, loss of signal frequency information, and easy occurrence of endpoint effects

Active Publication Date: 2015-11-04
BEIJING JIAOTONG UNIV
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Problems solved by technology

The discriminant algorithm for the number of Intrinsic Mode Functions (IMF) extracted by empirical mode decomposition is not perfect, and it is prone to end-point effects, and the frequency information of the signal is lost, which affects the diagnostic accuracy. The algorithm of the Kalbert spectrum has high time complexity, which is not conducive to practical operation

Method used

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  • Rolling bearing fault prediction method based on wavelet principal component analysis
  • Rolling bearing fault prediction method based on wavelet principal component analysis
  • Rolling bearing fault prediction method based on wavelet principal component analysis

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Embodiment 1

[0045] Wavelet packet analysis is a signal processing method based on the time-frequency domain. Its good local optimization properties make wavelet packet analysis show multi-scale and ability to detect sudden changes in the processing of non-stationary signals. research hotspot.

[0046] This embodiment provides a processing flow of a rolling bearing fault prediction method based on wavelet-PCA technology as follows figure 1 As shown, the following processing steps are included:

[0047] Step S110, the model builder constructs the fusion feature space, and uses the multi-resolution state fusion space as a specific model analysis through the processor.

[0048] The mathematical model of the above-mentioned state fusion space can be expressed in the following form: assuming a time-domain signal f(t), t∈N, after transformation in the Euclidean space, F(λ)=Γ[f(t)] can be obtained, where , λ={λ 1 ,...λ i ,...λ n} is an n-dimensional Cartesian set, and the n-dimensional Carte...

Embodiment 2

[0072] Firstly, the vibration acceleration signal of the rolling bearing of the train is extracted. In this example, the sampling frequency is 12Khz, and the number of periodic sampling is 4096 times. The signal is filtered using a wavelet filter function. figure 2 Shown are the filtered roller fault and bearing vibration acceleration time-domain signals for normal conditions.

[0073] Then, the wavelet transformation is performed on the vibration acceleration signal of the rolling bearing, and the db3 wavelet base is used to decompose the 4-layer wavelet packet to obtain 16 wavelet packet decomposition coefficients and 16 wavelet subband coefficients. The 16 wavelet subband coefficients extract their correlation coefficient entropy respectively, such as image 3 shown.

[0074] Then all the correlation coefficient entropy is fused with the PCA feature state fuser, and the principal component ratio extracted based on the wavelet packet correlation coefficient entropy is as ...

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Abstract

The invention provides a rolling bearing fault prediction method based on wavelet principal component analysis. The method mainly comprises the following steps: extracting a wavelet packet transform coefficient of a vibration acceleration signal of the rolling bearing, and calculating a multiresolution similarity coefficient entropy between the wavelet packet transform coefficient and the vibration acceleration signal of the rolling bearing; and applying a wavelet principal component analysis algorithm to carry out feature fusion processing on the multiresolution similarity coefficient entropy, obtaining the fusion feature measure of the vibration acceleration signal of the rolling bearing of a multiresolution state fusion space, and identifying a fusion interval of the rolling bearings under a normal state, a fault state and a hidden danger state. The rolling bearing fault prediction method adopts the PCA (Principal Component Analysis) algorithm to carry out hidden danger identification on hidden dangers and carries out positioning classification on the vibration signals of a motor bearing subjected to the hidden danger of heterology by a thought that multi-dimensional state principle components are extracted by the PCA algorithm on the basis of the separability of the vibration features of the hidden danger of the heterology of the bearing, and experiments prove the effectiveness of a bearing hidden danger monitoring and positioning method based on the multiresolution state fusion space.

Description

technical field [0001] The invention relates to the technical field of rolling bearings, in particular to a rolling bearing fault prediction method based on wavelet principal component analysis. Background technique [0002] In the 1960s, fault diagnosis of rolling bearings began to appear in the field of science and technology. After decades of rapid development, it has now become a comprehensive applied discipline that combines the fields of mechanical detection, automatic control, and pattern recognition. [0003] Rolling bearings play a vital role in many electromechanical equipment, and the working condition of rolling bearings is also a key factor affecting the state of equipment. Due to the complex working conditions of the equipment and the instability of the environmental parameters, it is difficult to identify the cause of the failure of the rolling bearing. In addition, rolling bearings are composed of units such as outer rings, inner rings, rollers, and cages. T...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 贾利民付云骁秦勇
Owner BEIJING JIAOTONG UNIV
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