189 related articles for article (PubMed ID: 37770368)
1. A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy.
Xiao M; Wang Z; Zhao Y; Geng G; Dustdar S; Donta PK; Ji G
ISA Trans; 2023 Dec; 143():536-547. PubMed ID: 37770368
[TBL] [Abstract][Full Text] [Related]
2. Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy.
Zhang W; Zhou J
Entropy (Basel); 2019 May; 21(5):. PubMed ID: 33267235
[TBL] [Abstract][Full Text] [Related]
3. Fault Feature Extraction Method for Rolling Bearings Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition.
Wang L; Li H; Xi T; Wei S
Sensors (Basel); 2023 Nov; 23(23):. PubMed ID: 38067814
[TBL] [Abstract][Full Text] [Related]
4. Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing.
Yang F; Kou Z; Wu J; Li T
Entropy (Basel); 2018 Sep; 20(9):. PubMed ID: 33265756
[TBL] [Abstract][Full Text] [Related]
5. Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree.
Mao Y; Xin J; Zang L; Jiao J; Xue C
Entropy (Basel); 2024 Feb; 26(3):. PubMed ID: 38539734
[TBL] [Abstract][Full Text] [Related]
6. Weak Fault Feature Extraction of Rolling Bearings Based on Adaptive Variational Modal Decomposition and Multiscale Fuzzy Entropy.
Lv Z; Han S; Peng L; Yang L; Cao Y
Sensors (Basel); 2022 Jun; 22(12):. PubMed ID: 35746283
[TBL] [Abstract][Full Text] [Related]
7. Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests.
Xue X; Li C; Cao S; Sun J; Liu L
Entropy (Basel); 2019 Jan; 21(1):. PubMed ID: 33266812
[TBL] [Abstract][Full Text] [Related]
8. Hierarchical Amplitude-Aware Permutation Entropy-Based Fault Feature Extraction Method for Rolling Bearings.
Li Z; Cui Y; Li L; Chen R; Dong L; Du J
Entropy (Basel); 2022 Feb; 24(3):. PubMed ID: 35327821
[TBL] [Abstract][Full Text] [Related]
9. Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM.
Ye M; Yan X; Jia M
Entropy (Basel); 2021 Jun; 23(6):. PubMed ID: 34208777
[TBL] [Abstract][Full Text] [Related]
10. Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology.
Xue Z; Huang Y; Zhang W; Shi J; Luo H
Sensors (Basel); 2023 Oct; 23(21):. PubMed ID: 37960468
[TBL] [Abstract][Full Text] [Related]
11. GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction.
Ding J; Huang L; Xiao D; Li X
Sensors (Basel); 2020 Mar; 20(7):. PubMed ID: 32244305
[TBL] [Abstract][Full Text] [Related]
12. Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm.
Shi R; Wang B; Wang Z; Liu J; Feng X; Dong L
Entropy (Basel); 2022 Jun; 24(6):. PubMed ID: 35741545
[TBL] [Abstract][Full Text] [Related]
13. Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy.
Bie F; Shu Y; Lyu F; Liu X; Lu Y; Li Q; Zhang H; Ding X
Sensors (Basel); 2024 Jan; 24(3):. PubMed ID: 38339442
[TBL] [Abstract][Full Text] [Related]
14. A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing.
Liang T; Lu H
Entropy (Basel); 2020 Sep; 22(9):. PubMed ID: 33286764
[TBL] [Abstract][Full Text] [Related]
15. The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy.
Zhuang D; Liu H; Zheng H; Xu L; Gu Z; Cheng G; Qiu J
Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679788
[TBL] [Abstract][Full Text] [Related]
16. A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy.
Zhang Y; Lv Y; Ge M
Entropy (Basel); 2021 Feb; 23(2):. PubMed ID: 33562457
[TBL] [Abstract][Full Text] [Related]
17. Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest.
Liu A; Yang Z; Li H; Wang C; Liu X
Sensors (Basel); 2022 Mar; 22(5):. PubMed ID: 35271193
[TBL] [Abstract][Full Text] [Related]
18. Early Fault Detection of Rolling Bearings Based on Time-Varying Filtering Empirical Mode Decomposition and Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted.
Song S; Wang W
Entropy (Basel); 2023 Oct; 25(10):. PubMed ID: 37895573
[TBL] [Abstract][Full Text] [Related]
19. A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition.
Zhang W; Zhou J
Entropy (Basel); 2019 Jul; 21(7):. PubMed ID: 33267394
[TBL] [Abstract][Full Text] [Related]
20. A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction.
Ma J; Han S; Li C; Zhan L; Zhang GZ
Entropy (Basel); 2021 Apr; 23(4):. PubMed ID: 33920417
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
