ArticleEntropy (Basel, Switzerland)2019
Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Fault Diagnosis Method of Planetary Gearboxes Based on Multi-Scale Wavelet Packet Energy Entropy and Extreme Learning Machine.Entropy (Basel, Switzerland) · 2025Article
- Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation.Entropy (Basel, Switzerland) · 2021Article
- A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy.Entropy (Basel, Switzerland) · 2020Article
- A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis.Entropy (Basel, Switzerland) · 2019Article
- An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage.Entropy (Basel, Switzerland) · 2019Article
- Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis.Entropy (Basel, Switzerland) · 2019Article
- An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing.Entropy (Basel, Switzerland) · 2019Article
- Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing.Entropy (Basel, Switzerland) · 2019Article
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4 authors.
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Abstract
Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. In the end, both the SWPDE-KELM and the SWPPE-KELM methods are evaluated on two bearing vibration signal databases. We compare these two feature extraction methods to a recently proposed method called stationary wavelet packet singular value entropy (SWPSVE). Based on our results, we can say that the diagnosis accuracy obtained by the SWPDE-KELM method is slightly better than the SWPPE-KELM method and they both significantly outperform the SWPSVE-KELM method.
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