ArticleEntropy (Basel, Switzerland)2020
A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT.Entropy (Basel, Switzerland) · 2023Article
- Research on Structurally Constrained KELM Fault-Diagnosis Model Based on Frequency-Domain Fuzzy Entropy.Entropy (Basel, Switzerland) · 2023Article
- Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method.Scientific reports · 2022Article
- Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects.Sensors (Basel, Switzerland) · 2022Article
- Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features.Entropy (Basel, Switzerland) · 2022Article
- Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE-MACNN.Entropy (Basel, Switzerland) · 2022Article
- A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals.Sensors (Basel, Switzerland) · 2022Article
- Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
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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%.
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