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A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy
- Source :
- Entropy, Vol 22, Iss 3, p 290 (2020), Entropy, Volume 22, Issue 3
- Publication Year :
- 2020
-
Abstract
- Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.
- Subjects :
- eemd
Computer science
Noise reduction
Feature extraction
General Physics and Astronomy
lcsh:Astrophysics
02 engineering and technology
Article
Hilbert–Huang transform
wavelet semi-soft threshold
multi-scale entropy
symbols.namesake
Wavelet
lcsh:QB460-466
0202 electrical engineering, electronic engineering, information engineering
Entropy (information theory)
lcsh:Science
business.industry
Signal reconstruction
020208 electrical & electronic engineering
Pattern recognition
fault diagnosis
lcsh:QC1-999
Pearson product-moment correlation coefficient
Support vector machine
rolling bearing
symbols
lcsh:Q
020201 artificial intelligence & image processing
Artificial intelligence
business
lcsh:Physics
Subjects
Details
- ISSN :
- 10994300
- Volume :
- 22
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Entropy (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....2e5d7810055998591481cfcc216d556d