Abstract
Since the vibration signals of gearbox are non-linear and non-stationary, it is difficult to accurately evaluate the working conditions. Therefore, a fault feature extraction technique based on intrinsic characteristic-scale decomposition (ICD) and multi-scale entropy (MSE) is presented in this paper. The measured signals are firstly decomposed into a series of product components (PCs) by ICD. Secondly, the main product component is selected, and then MSE is used to extract the feature vectors from the selected PCs. Finally, the obtained feature vectors of gearbox with different scale factors are adopted as inputs of support vector machine (SVM) to fulfill the fault patterns identification. The superiority of the proposed technique is verified through comparing with three other methods.
| Original language | English |
|---|---|
| Pages (from-to) | 3596-3607 |
| Number of pages | 12 |
| Journal | Journal of Vibroengineering |
| Volume | 18 |
| Issue number | 6 |
| DOIs | |
| State | Published - 2016 |
| Externally published | Yes |
Keywords
- Fault feature extraction
- Gearbox
- Intrinsic characteristic-scale decomposition
- Multiscale entropy
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