138 related articles for article (PubMed ID: 28773035)
1. Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.
Xue L; Li N; Lei Y; Li N
Materials (Basel); 2017 Jun; 10(6):. PubMed ID: 28773035
[TBL] [Abstract][Full Text] [Related]
2. Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds.
Zhao M; Lin J; Xu X; Lei Y
Sensors (Basel); 2013 Aug; 13(8):10856-75. PubMed ID: 23959244
[TBL] [Abstract][Full Text] [Related]
3. Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps.
Tayyab SM; Chatterton S; Pennacchi P
Sensors (Basel); 2022 Mar; 22(5):. PubMed ID: 35271173
[TBL] [Abstract][Full Text] [Related]
4. Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution.
Jia F; Lei Y; Shan H; Lin J
Sensors (Basel); 2015 Nov; 15(11):29363-77. PubMed ID: 26610501
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions.
Wan H; Gu X; Yang S; Fu Y
Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991841
[TBL] [Abstract][Full Text] [Related]
8. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.
Cheng Y; Zhou B; Lu C; Yang C
Materials (Basel); 2017 May; 10(6):. PubMed ID: 28772943
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Research on Extraction of Compound Fault Characteristics for Rolling Bearings in Wind Turbines.
Xiang L; Su H; Li Y
Entropy (Basel); 2020 Jun; 22(6):. PubMed ID: 33286455
[TBL] [Abstract][Full Text] [Related]
11. Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis.
Zhao M; Lin J; Xu X; Li X
Sensors (Basel); 2014 Oct; 14(11):20320-46. PubMed ID: 25353982
[TBL] [Abstract][Full Text] [Related]
12. WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing.
Liu R; Wang X; Kumar A; Sun B; Zhou Y
Micromachines (Basel); 2023 Jul; 14(7):. PubMed ID: 37512779
[TBL] [Abstract][Full Text] [Related]
13. Periodicity-enhanced sparse representation for rolling bearing incipient fault detection.
Yao R; Jiang H; Wu Z; Wang K
ISA Trans; 2021 Dec; 118():219-237. PubMed ID: 33676735
[TBL] [Abstract][Full Text] [Related]
14. University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets.
Sehri M; Dumond P; Bouchard M
Data Brief; 2023 Aug; 49():109327. PubMed ID: 37435140
[TBL] [Abstract][Full Text] [Related]
15. Iterative characteristic ridge extraction for bearing fault detection under variable rotational speed conditions.
Li Y; Yang Y; Chen Y; Chen Z
ISA Trans; 2022 Jan; 119():172-183. PubMed ID: 33676740
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. A Bearing Fault Diagnosis Method Based on PAVME and MEDE.
Yan X; Xu Y; She D; Zhang W
Entropy (Basel); 2021 Oct; 23(11):. PubMed ID: 34828100
[TBL] [Abstract][Full Text] [Related]
18. Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism.
Wu H; Li J; Zhang Q; Tao J; Meng Z
ISA Trans; 2022 Nov; 130():477-489. PubMed ID: 35491253
[TBL] [Abstract][Full Text] [Related]
19. On the Accuracy of Fault Diagnosis for Rolling Element Bearings Using Improved DFA and Multi-Sensor Data Fusion Method.
Song Q; Zhao S; Wang M
Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33198252
[TBL] [Abstract][Full Text] [Related]
20. Application of Teager-Kaiser Energy Operator in the Early Fault Diagnosis of Rolling Bearings.
Shi X; Zhang Z; Xia Z; Li B; Gu X; Shi T
Sensors (Basel); 2022 Sep; 22(17):. PubMed ID: 36081131
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]