Name
Abstract | ||
|---|---|---|
During the movement of rail trains, trains are often subjected to harsh operating conditions such as variable speed and heavy loads. It is therefore vital to find a solution for the issue of rolling bearing malfunction diagnostics in such circumstances. This study proposes an adaptive technique for defect identification based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Ramanujan subspace decomposition. MOMEDA optimally filters the signal and enhances the shock component corresponding to the defect, after which the signal is automatically decomposed into a sequence of signal components using Ramanujan subspace decomposition. The method's benefit stems from the flawless integration of the two methods and the addition of the adaptable module. It addresses the issues that the conventional signal decomposition and subspace decomposition methods have with redundant parts and significant inaccuracies in fault feature extraction for the vibration signals under loud noise. Finally, it is evaluated through simulation and experimentation in comparison to the current widely used signal decomposition techniques. According to the findings of the envelope spectrum analysis, the novel technique can precisely extract the composite flaws that are present in the bearing, even when there is significant noise interference. Additionally, the signal-to-noise ratio (SNR) and fault defect index were introduced to quantitatively demonstrate the novel method's denoising and potent fault extraction capabilities, respectively. The approach works well for identifying bearing faults in train wheelsets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/e24101381 | ENTROPY |
Keywords | DocType | Volume |
rolling bearing, compound fault, Ramanujan subspace decomposition, fault feature extraction | Journal | 24 |
Issue | ISSN | Citations |
10 | 1099-4300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Runtao Sun | 1 | 0 | 0.34 |
| Jianwei Yang | 2 | 1 | 4.08 |
| Dechen Yao | 3 | 0 | 0.34 |
| Jinhai Wang | 4 | 0 | 1.01 |