A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains
Abstract
:1. Introduction
2. Sensor Array Design and Impact Point Positioning
2.1. The Process of Signal Acquisition
Sensor Selection
2.2. Wireless Transmitter Modules
Signal Conditioning Circuit
2.3. Implementation and Field Test
3. The Proposed MCA Technique for Bearing Fault Detection
3.1. Overview
- (1)
- Decompose the collected vibration signal into the upper computer conditioning program; and then formulate a group of IMFs by using the VMD.
- (2)
- Compute the normalized correlation measure (NCM), the mutual information analysis (MIA), and the envelope correlation spectrum (ECS) for each IMF.
- (3)
- Calculate the cumulation values and average values of NCM, MIA, and ECS. If the cumulation value is less than the average, the corresponding IMF will be discarded. If the cumulation value is more than the average value, the IMF will be used to formulate an analytical signal.
- (4)
- Conduct spectral analysis of the analytical signal for bearing fault detection.
3.2. Variational Mode Decomposition
- (1)
- Initialize , , , and the maximum number of iterations N, ;
- (2)
- Use Equations (3) and (4) to update and ;
- (3)
- Use Equation (5) to update ;
- (4)
- The criterion of precision convergence is , where is a small positive number over (0, 0.01] (or up to 1%). = 0.001 is used in this work. If or the iteration is completed. Otherwise return to Step (2) and continue until the convergence conditions are satisfied.
3.3. MCA for IMF Selection
- (1)
- The number of local extrema (minima and maxima) and the number of zero crossings must either be equal or differ at most by one in the whole data set;
- (2)
- The mean value of the envelope defined by the local extrema is zero.
3.3.1. Normalized Correlation Measure (NCM)
3.3.2. Mutual Information Analysis (MIA)
3.3.3. Envelope Correlation Spectrum (ECS)
3.4. Signal Reconstruction
4. Performance Evaluation Testing
4.1. Experimental Setup
- (1)
- The first technique for comparison is the Synchronous Influence Index (SII) method [23], which can assess the complex impulsive fault component, will be applied for comparison, as specified as SII;
- (2)
- The second method used for comparison is the frequency band entropy (FBE) analysis technique [17]. Its IMF section is based on an optimal algorithm that contains abundant fault information;
- (3)
- The third method for comparison is the general Hilbert–Huang (HH) transform technique. It uses the common IMF selection method or to choose the first two IMFs for analysis, which is designated as HH-C;
- (4)
- The fourth method used for comparison uses the general HH transform technique, but using kurtosis to choose two IMFs (with the highest kurtosis values) for analysis, designated as HH-K;
- (5)
- The fifth method applied for comparison is designated as NME, which uses the highest correlation values of the sum of the NCM, MIA, and ECS values, rather than the comprehensive assessment of these values by using the proposed MCA technique.
4.2. MCA Technique Implementation
4.3. Experimental Results Discussion
4.3.1. Healthy Bearing Condition Monitoring
4.3.2. Outer Race Fault Detection
4.3.3. Inner Race Fault Detection
4.3.4. Rolling Element (Ball) Defect Detection
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Bearing Type | MB ER-10K |
---|---|
Inside diameter (mm) | 25.001 |
Outside diameter (mm) | 51.999 |
Thickness(mm) | 15.001 |
Pitch diameter (mm) | 39.034 |
Rolling element diameter (mm) | 7.940 |
Number of rolling elements | 9 |
Contact angle (degrees) | 0 |
Bearing Condition | Characteristic Frequency (Hz) |
---|---|
Normal/healthy bearing | fr |
Outer race defect | 3.58 fr |
Inner race defect | 5.41 fr |
Rolling element defect | 4.71 fr |
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Sun, S.; Zhang, S.; Wang, W. A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains. Sensors 2023, 23, 6392. https://doi.org/10.3390/s23146392
Sun S, Zhang S, Wang W. A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains. Sensors. 2023; 23(14):6392. https://doi.org/10.3390/s23146392
Chicago/Turabian StyleSun, Sitong, Sheng Zhang, and Wilson Wang. 2023. "A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains" Sensors 23, no. 14: 6392. https://doi.org/10.3390/s23146392
APA StyleSun, S., Zhang, S., & Wang, W. (2023). A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains. Sensors, 23(14), 6392. https://doi.org/10.3390/s23146392