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Bearing vibration features 3. The system under investigation

Table 1 Bearing vibration features 3. The system under investigation

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Studying in frequency domain shows that when a fault is introduced to a bearing some of the frequencies explained above appear in the vibration signals as shown in Table 1 [7].
Studying in frequency domain shows that when a fault is introduced to a bearing some of the frequencies explained above appear in the vibration signals as shown in Table 1 [7].
Fig. 1. Basic frequency in a bearing.
Fig. 1. Basic frequency in a bearing.
Fig. 2. Structure of a ball bearing and the definition of each variable.
Fig. 2. Structure of a ball bearing and the definition of each variable.
Here N is the signal length and o is the noise standard deviation, and the soft threshold was employed.
Here N is the signal length and o is the noise standard deviation, and the soft threshold was employed.
Fig. 3. The test stand, picture by permission of Dr. Kenneth A. Loparo, Case Western Reverse Lab. Derive end bearing: 6205-2RS JEM SKF, deep groove ball bearing
Fig. 3. The test stand, picture by permission of Dr. Kenneth A. Loparo, Case Western Reverse Lab. Derive end bearing: 6205-2RS JEM SKF, deep groove ball bearing
Bearing vibration features of the system under the investigation Fig. 4. Meyer wavelet transform: (A) synthesis wavelet for decomposition (detail) and (B) synthesis scaling for decomposition (approximation).
Bearing vibration features of the system under the investigation Fig. 4. Meyer wavelet transform: (A) synthesis wavelet for decomposition (detail) and (B) synthesis scaling for decomposition (approximation).
Fig. 6. Signal from drive end bearing with the outer-race damaged after denoising. Fig. 5. Signal from drive end bearing with the outer-race damaged.
Fig. 6. Signal from drive end bearing with the outer-race damaged after denoising. Fig. 5. Signal from drive end bearing with the outer-race damaged.
Fig. 7. Spectrum of the signal in Fig. 6.
Fig. 7. Spectrum of the signal in Fig. 6.
Fig. 8. Topology of the SVM machine with RBF kernels. Three common types of kernels used in SVMs Table 4
Fig. 8. Topology of the SVM machine with RBF kernels. Three common types of kernels used in SVMs Table 4
Fig. 10. Sensitivity diagram. Fig. 9. Learning curve of support vector machine.
Fig. 10. Sensitivity diagram. Fig. 9. Learning curve of support vector machine.
In this investigation we provide a procedure for fault classification of rolling bearings, using SVM classifier. Vibration data from bearings were denoised using discrete Meyer wavelet. Using the data from this procedure,
In this investigation we provide a procedure for fault classification of rolling bearings, using SVM classifier. Vibration data from bearings were denoised using discrete Meyer wavelet. Using the data from this procedure,
SVM output for tested data Table 5
SVM output for tested data Table 5
A compressive study between the current work and some recent publications Table 6
A compressive study between the current work and some recent publications Table 6
Related topics:
Mechanical EngineeringCivil EngineeringWavelet AnalysisElectric MotorFault diagnosisTime Frequency AnalysisInterdisciplinary Engineering
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