402 related articles for article (PubMed ID: 33267235)
1. 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]
2. 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]
3. A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.
Zhou S; Qian S; Chang W; Xiao Y; Cheng Y
Sensors (Basel); 2018 Jun; 18(6):. PubMed ID: 29899216
[TBL] [Abstract][Full Text] [Related]
4. Use of Composite Multivariate Multiscale Permutation Fuzzy Entropy to Diagnose the Faults of Rolling Bearing.
Yuan Q; Lv M; Zhou R; Liu H; Liang C; Cheng L
Entropy (Basel); 2023 Jul; 25(7):. PubMed ID: 37509995
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
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. A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy.
Ge J; Niu T; Xu D; Yin G; Wang Y
Entropy (Basel); 2020 Mar; 22(3):. PubMed ID: 33286065
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis.
Zhen D; Guo J; Xu Y; Zhang H; Gu F
Sensors (Basel); 2019 Sep; 19(18):. PubMed ID: 31527448
[TBL] [Abstract][Full Text] [Related]
11. Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing.
Ying W; Tong J; Dong Z; Pan H; Liu Q; Zheng J
Entropy (Basel); 2022 Jan; 24(2):. PubMed ID: 35205457
[TBL] [Abstract][Full Text] [Related]
12. Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals.
Wang Z; Yao L; Chen G; Ding J
ISA Trans; 2021 Aug; 114():470-484. PubMed ID: 33454055
[TBL] [Abstract][Full Text] [Related]
13. An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis.
Cheng Y; Wang Z; Chen B; Zhang W; Huang G
ISA Trans; 2019 Aug; 91():218-234. PubMed ID: 30738582
[TBL] [Abstract][Full Text] [Related]
14. A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine.
Yan X; Liu Y; Jia M
Sensors (Basel); 2020 Aug; 20(15):. PubMed ID: 32759788
[TBL] [Abstract][Full Text] [Related]
15. Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy.
Yan X; Xu Y; Jia M
Entropy (Basel); 2021 Aug; 23(9):. PubMed ID: 34573753
[TBL] [Abstract][Full Text] [Related]
16. Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings.
Tu D; Zheng J; Jiang Z; Pan H
Entropy (Basel); 2018 May; 20(5):. PubMed ID: 33265449
[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. 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]
19. 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]
20. Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator.
Ma J; Wu J; Wang X
ISA Trans; 2018 Sep; 80():297-311. PubMed ID: 29880275
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]