Bearing Fault Diagnosis Based on Labview

Bearing Fault Diagnosis Based on Labview

Wan-ye Yao (North China Electric Power University, Beijing, China) and Xue-Li Jiang (North China Electric Power University, Beijing, China)
DOI: 10.4018/IJAPUC.2015070103
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Abstract

This function of wavelet packet decomposition and the energy of each band to strike is achieved within the Labview module. Signal energy in different frequency bands within the change reflects a change in the operating state. Extract wavelet packet energy spectrum of each band, making it as a feature vector. Finally the fault are classified by SVM. The two Parameters, the kernel function parameters g of radial machine support vector machine and penalty factor C of the radial machine support vector machine, are optimally chosen, automatically and rapidly, by using the method of particle swarm algorithm, avoiding the blindness of artificial selection parameters. The Matalab program of support vector machines based on particle swarm optimization are made into COM components. Mixed programming, Labview call COM component, generated by the M file, is implemented, which is divorced from the MATLAB environment, making it good for expanding the function of Labview. The effectiveness, wavelet packet energy spectrum - PSOSVM model of the bearing fault diagnosis, is verified.
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2. The Principle Of Wavelet Packet And Calculate The Energy Spectrum

2.1 The Principle of Wavelet Packet

The algorithm of Wavelet packet decomposition is

,

Wavelet reconstruction algorithm is as follows:

,

is Said the first K coefficient of m-th sub-band, j-th layer of wavelet packet decomposition. With the above signal wavelet packet decomposition algorithm, when the layer of wavelet packet decomposition is N, we can obtain 2N sub-bands.

2.2 Distribution of Energy Based on Wavelet Packet Decomposition

The algorithm of wavelet packet energy distribution algorithm is as followings [6]:

  • 1.

    Three-layer wavelet packet decomposition of vibration signal, get Wavelet packet coefficients from low to high frequency sub-bands 8 in layer 3.

  • 2.

    Reconstruction of the wavelet coefficients. Extracting each sub-band range signal (j = 0, 1,.. 7).

  • 3.

    Calculate the sub-band signal energy. (j = 0,1,, 7) represents the reconstructed signal of the third layer of each node, the corresponding energy, (j = 0,1 ... 7;K = 0,1 ... n) for the amplitude of the reconstructed signal at discrete points.

  • 4.

    Structural feature vectors. T eigenvectors constructed as follows: .

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