Works matching DE "ROLLER bearings"
Results: 2307
Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 7, p. 3523, doi. 10.1007/s10845-023-02221-1
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Early fault diagnosis in rolling element bearings: comparative analysis of a knowledge-based and a data-driven approach.
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- Journal of Intelligent Manufacturing, 2024, v. 35, n. 5, p. 2327, doi. 10.1007/s10845-023-02151-y
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A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 5, p. 2449, doi. 10.1007/s10845-022-01944-x
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Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings.
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- Journal of Intelligent Manufacturing, 2023, v. 34, n. 5, p. 2225, doi. 10.1007/s10845-022-01910-7
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Experimental analysis of factors influencing the cage slip in cylindrical roller bearing.
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- International Journal of Advanced Manufacturing Technology, 2011, v. 53, n. 5-8, p. 635, doi. 10.1007/s00170-010-2854-5
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A workholding optimization model for turning of ring-shaped parts.
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- International Journal of Advanced Manufacturing Technology, 2007, v. 32, n. 7/8, p. 656, doi. 10.1007/s00170-005-0399-9
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轻载装载机变速箱振动特性分析与试验.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 17, p. 94, doi. 10.3969/j.issn.1001-3881.2023.17.016
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基于多通道循环平稳特征融合的智能诊断方法.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 16, p. 222, doi. 10.3969/j.issn.1001-3881.2023.16.033
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基于ADASYN 和 Swin Transformer 的滚动轴承故障诊断研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 15, p. 209, doi. 10.3969/j.issn.1001-3881.2023.15.034
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一种基于 CNN 的滚动轴承退化指标构建方法.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 15, p. 187, doi. 10.3969/j.issn.1001-3881.2023.15.030
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改进的注意机制在滚动轴承故障诊断中的应用.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 12, p. 216, doi. 10.3969/j.issn.1001-3881.2023.12.034
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基于CEEMDAN 和层次波动离散熵的滚动轴承声音信号故障检测.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 12, p. 195, doi. 10.3969/j.issn.1001-3881.2023.12.031
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基于 WTD-AR 谱和 MEA-BPNN 的轴承故障诊断方法.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 11, p. 188, doi. 10.3969/j.issn.1001-3881.2023.11.031
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基于 KECA 和 BO-SVDD 的滚动轴承早期故障检测.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 11, p. 206, doi. 10.3969/j.issn.1001-3881.2023.11.034
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基于时频熵特征实现异步电机机械故障诊断.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 10, p. 215, doi. 10.3969/j.issn.1001-3881.2023.10.037
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SVD-LESE 在滚动轴承微弱故障识别中的应用研究.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 10, p. 210, doi. 10.3969/j.issn.1001-3881.2023.10.036
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基于时域和谱峭度特征融合及指数模型的滚动轴承 RUL 预测.
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- Machine Tool & Hydraulics, 2023, v. 51, n. 10, p. 203, doi. 10.3969/j.issn.1001-3881.2023.10.035
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A LIKELIHOOD CONTROL CHART FOR MONITORING BIVARIATE LIFETIME PROCESSES.
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- Journal of Mahani Mathematical Research Center, 2022, v. 11, n. 2, p. 97, doi. 10.22103/jmmrc.2022.19093.1209
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基于改进CNN的轴承声学故障诊断.
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- Electronic Science & Technology, 2023, v. 36, n. 1, p. 75, doi. 10.16180/j.cnki.issn1007-7820.2023.01.011
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基于 MATLAB App Designer 的牵引电机滚动轴承可靠性评估系统设计.
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- Electronic Science & Technology, 2022, v. 35, n. 3, p. 79, doi. 10.16180/j.cnki.issn1007-7820.2022.03.012
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Reinforcement Learning for Rolling Bearing Fault Diagnosis--A Comprehensive Review.
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- Journal Européen des Systèmes Automatisés, 2024, v. 57, n. 4, p. 1185, doi. 10.18280/jesa.570425
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Accelerated wear testing shows that thermoplastic bushings could be a cost-effective and durable alternative to traditional bearings for wheelchair caster use.
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- Journal of Rehabilitation & Assistive Technologies Engineering, 2022, p. 1, doi. 10.1177/20556683221144805
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Rolling element bearing design through genetic algorithms.
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- Engineering Optimization, 2003, v. 35, n. 6, p. 649, doi. 10.1080/03052150310001624403
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ВДОСКОНАЛЕННЯ ПРИСТРОЇВ ЗАХИСТУ РОБОЧИХ ВУЗЛІВ ГРЕЙФЕРІВ.
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- Automation of Technological & Business Processes / Avtomatizaciâ Tehnologiceskih i Biznes-Processov, 2020, v. 12, n. 3, p. 46, doi. 10.15673/atbp.v12i3.1728
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Fault Diagnosis Feature Extraction of Marine Rolling Bearing Based on MEMD and Pe.
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- Journal of Coastal Research, 2021, v. 94, n. sp1, p. 342, doi. 10.2112/SI94-070.1
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Generation of reproducible and scalable local damage to rolling bearings for the development of condition monitoring systems through the use of laser technology.
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- Technisches Messen, 2022, v. 89, n. 11, p. 810, doi. 10.1515/teme-2022-0083
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Impact Analysis of Roller System Stability for Four-High Mill Horizontal Vibration.
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- Shock & Vibration, 2016, p. 1, doi. 10.1155/2016/5693584
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A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition.
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- Shock & Vibration, 2016, p. 1, doi. 10.1155/2016/9372691
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A Two-Stage Compression Method for the Fault Detection of Roller Bearings.
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- Shock & Vibration, 2016, p. 1, doi. 10.1155/2016/2971749
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Chaotic Dynamics of Cage Behavior in a High-Speed Cylindrical Roller Bearing.
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- Shock & Vibration, 2016, p. 1, doi. 10.1155/2016/9120505
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Modeling and Dynamic Analysis of Spherical Roller Bearing with Localized Defects: Analytical Formulation to Calculate Defect Depth and Stiffness.
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- Shock & Vibration, 2016, p. 1, doi. 10.1155/2016/2106810
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Roller Bearing Monitoring by New Subspace-Based Damage Indicator.
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- Shock & Vibration, 2015, p. 1, doi. 10.1155/2015/828093
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Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects.
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- Shock & Vibration, 2015, p. 1, doi. 10.1155/2015/814650
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Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing.
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- Shock & Vibration, 2015, v. 2015, p. 1, doi. 10.1155/2015/847802
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- Article
A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM.
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- Shock & Vibration, 2014, p. 1, doi. 10.1155/2014/825825
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Dynamic modeling and analysis of the large-scale rotary machine with multi-supporting.
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- Shock & Vibration, 2011, v. 18, n. 1/2, p. 53, doi. 10.1155/2011/541049
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Degradation assessment and fault diagnosis for roller bearing based on AR model and fuzzy cluster analysis.
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- Shock & Vibration, 2011, v. 18, n. 1/2, p. 127, doi. 10.1155/2011/703210
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Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery.
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- Shock & Vibration, 2009, v. 16, n. 1, p. 89, doi. 10.1155/2009/519502
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A Study on the Possibility of Replacing Roller Bearings in CM 120L Concrete Mixers with Necuron 1050 Sliding Bearings.
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- Engineering, Technology & Applied Science Research, 2025, v. 15, n. 1, p. 19358, doi. 10.48084/etasr.9462
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Numerical Assessment of Bidirectional Roller Bearing Isolators under Near-Fault Earthquakes.
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- Revista Ingeniería e Investigación, 2024, v. 44, n. 2, p. 1, doi. 10.15446/ing.investig.104078
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Acoustic emission-based early fault detection in tapered roller bearings.
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- Revista Ingeniería e Investigación, 2013, v. 33, n. 3, p. 5, doi. 10.15446/ing.investig.v33n3.41032
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Fault diagnosis of printing press bearing based on deformable convolution residual neural network.
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- Networks & Heterogeneous Media, 2023, v. 18, n. 2, p. 1, doi. 10.3934/nhm.2023027
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Rolling Bearing Reliability Prediction Based on Signal Noise Reduction and RHA-MKRVM.
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- International Journal of Advanced Computer Science & Applications, 2024, v. 15, n. 8, p. 1196, doi. 10.14569/ijacsa.2024.01508116
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Quantum-behaved particle swarm optimization of convolutional neural network for fault diagnosis.
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- Journal of Experimental & Theoretical Artificial Intelligence, 2024, v. 36, n. 7, p. 1035, doi. 10.1080/0952813X.2022.2120089
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Numerical Thermal Analysis of Greased Rolling Bearing Considering Surface Topography and Plastic Deformation.
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- Lubrication Science, 2025, v. 37, n. 2, p. 129, doi. 10.1002/ls.1730
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Research on the lubrication performance of circular tilting pad thrust bearings for large wind turbines.
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- Lubrication Science, 2024, v. 36, n. 6, p. 421, doi. 10.1002/ls.1681
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Interaction of anti‐wear additive TCP with advanced bearing steels.
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- Lubrication Science, 2022, v. 34, n. 3, p. 169, doi. 10.1002/ls.1577
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Frictional power loss in solid-grease-lubricated needle roller bearing.
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- Lubrication Science, 2013, v. 25, n. 5, p. 351, doi. 10.1002/ls.1195
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Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN.
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- International Journal of Production Research, 2023, v. 61, n. 23, p. 8252, doi. 10.1080/00207543.2022.2122621
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A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions.
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- International Journal of Production Research, 2021, v. 59, n. 16, p. 4811, doi. 10.1080/00207543.2020.1808261
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