ArticleEntropy (Basel, Switzerland)2021
A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamic Monitoring of a Bridge from GNSS-RTK Sensor Using an Improved Hybrid Denoising Method.Sensors (Basel, Switzerland) · 2025Article
- Enhanced DWT for Denoising Heartbeat Signal in Non-Invasive Detection.Sensors (Basel, Switzerland) · 2025Article
- Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis.Sensors (Basel, Switzerland) · 2023Article
- Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM.Entropy (Basel, Switzerland) · 2022Article
- Special Issue "Complex Dynamic System Modelling, Identification and Control".Entropy (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods.
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