Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD
2. Feature Extraction and Dimensionality Reduction
2.1. Improved Variational Mode Decomposition
- Initialize and . Assign the original signal to , and assign the low-frequency mode to 0.
- Remove low-frequency modes. remove the low-frequency mode from at each run as follows:
- Perform VMD decomposition. The number of fixed decompositions K is set to 2, and the penalty factor parameter alpha is set to 2000. The relevant literature showed that the penalty factor demonstrates strong applicability when the penalty factor is set to 2000 and VMD is suitable for extracting low-frequency modal components when the penalty factor is beyond 2000. Therefore, the VMD was run one at a time to obtain both high- and low-frequency modes.
- Iteration stop judgment. Stop the iteration when the posting progress of central frequencies of the two modes obtained from the decomposition is less than the set threshold to obtain the final decomposed mode. The posting progress is defined as follows:
- Take the kurtosis. Mode is represented by , and the kurtosis is calculated for all modes . The maximum kurtosis value is denoted as .
- Initial mode classification. Fast Fourier transform (FFT) is performed on mode to obtain the corresponding spectrum of each mode as follows:
- Modal reorganization determination. On the basis of the kurtosis, the reorganization of the class with more than one mode is determined and the maximum kurtosis S of modes in the class is compared with the value of kurtosis . When , the modal classification in the class is considered to be dominated by noise, the modal reorganization in the class. When , all modalities in the class are combined with corresponding modalities , respectively, and, based on the above principle, the decision is made whether to reorganize or not. Finally, all modalities are outputted after regrouping.
2.2. Mode Selection Based on KCI Features
2.3. Defect Frequency Amplitude Ratio
2.4. Comprehensive Indicator Downscaling
3. Support Vector Data Description
4. Performance Degradation Assessment Based on Combined Metric Downscaling and SVDD
5. Experimental Analysis and Validation
5.1. Presentation of Experimental Data
5.2. Effectiveness of VMD Improvements
5.3. Comparative Analysis of the Effect of LLE Downscaling
5.4. Assessment of Performance Degradation of Full-Life Rolling Bearings
5.5. Fault Verification in All Stages of Bearing Degradation
- The original vibration signal is decomposed using the improved VMD method, and modalities selected by the weighted kurtosis index are demodulated via enveloping with evident filtering effect. This effect is beneficial for separating early fault characteristics from the disturbance noise and conducive to the extraction of feature indicators.
- Defect frequency amplitude ratio indicators, which are sensitive to early faults and more sensitive to early fault onset than traditional RMS indicators, are extracted to address the problem of strong sensitivity of effective feature indicators to the initial stage.
- LLE dimensionality reduction is carried out on extracted comprehensive feature indicators to extract the main features, and the SVDD degradation assessment model combined with the relative distance indicator is used in the degradation assessment of the whole-life bearing. The proposed method is important for online health monitoring and early warning of bearings in practical production.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Method||VMD||Improvement of VMD|
|Bearing condition||Early failure||Medium failure||Severe failure||Very severe failure|
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Xin, H.; Zhang, H.; Yang, Y.; Wang, J. Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD. Machines 2022, 10, 677. https://doi.org/10.3390/machines10080677
Xin H, Zhang H, Yang Y, Wang J. Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD. Machines. 2022; 10(8):677. https://doi.org/10.3390/machines10080677Chicago/Turabian Style
Xin, Hongwei, Haidong Zhang, Yanjun Yang, and Jianguo Wang. 2022. "Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD" Machines 10, no. 8: 677. https://doi.org/10.3390/machines10080677