Year |
Citation |
Score |
2020 |
Guo Y, Sheng S, Phillips C, Keller J, Veers P, Williams L. A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes Renewable and Sustainable Energy Reviews. 127: 109888. DOI: 10.1016/J.Rser.2020.109888 |
0.313 |
|
2019 |
Verstraeten T, Nowe A, Keller J, Guo Y, Sheng S, Helsen J. Fleetwide data-enabled reliability improvement of wind turbines Renewable & Sustainable Energy Reviews. 109: 428-437. DOI: 10.1016/J.Rser.2019.03.019 |
0.314 |
|
2017 |
Hong L, Qu Y, Dhupia JS, Sheng S, Tan Y, Zhou Z. A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement Mechanical Systems and Signal Processing. 94: 14-32. DOI: 10.1016/J.Ymssp.2017.02.024 |
0.356 |
|
2017 |
Gao Z, Sheng S. Real-time monitoring, prognosis, and resilient control for wind turbine systems Renewable Energy. 116: 1-4. DOI: 10.1016/J.Renene.2017.10.059 |
0.308 |
|
2016 |
Sheng S. Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective Tribology Transactions. 59: 149-162. DOI: 10.1080/10402004.2015.1055621 |
0.323 |
|
2014 |
Yan R, Chen X, Li W, Sheng S. Mathematical Methods and Modeling in Machine Fault Diagnosis Mathematical Problems in Engineering. 2014: 1-3. DOI: 10.1155/2014/516590 |
0.501 |
|
2014 |
Yampikulsakul N, Byon E, Huang S, Sheng S, You M. Condition Monitoring of Wind Power System With Nonparametric Regression Analysis Ieee Transactions On Energy Conversion. 29: 288-299. DOI: 10.1109/Tec.2013.2295301 |
0.326 |
|
2014 |
Zappalá D, Tavner PJ, Crabtree CJ, Sheng S. Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis Iet Renewable Power Generation. 8: 380-389. DOI: 10.1049/Iet-Rpg.2013.0177 |
0.344 |
|
2014 |
Hong L, Dhupia JS, Sheng S. An explanation of frequency features enabling detection of faults in equally-spaced planetary gearbox Mechanism and Machine Theory. 73: 169-183. DOI: 10.1016/J.Mechmachtheory.2013.10.014 |
0.357 |
|
2014 |
Luo H, Hatch C, Kalb M, Hanna J, Weiss A, Sheng S. Effective and accurate approaches for wind turbine gearbox condition monitoring Wind Energy. 17: 715-728. DOI: 10.1002/We.1595 |
0.342 |
|
2013 |
Greco A, Sheng S, Keller J, Erdemir A. Material wear and fatigue in wind turbine Systems Wear. 302: 1583-1591. DOI: 10.1016/J.Wear.2013.01.060 |
0.314 |
|
2012 |
Dempsey PJ, Sheng S. Investigation of data fusion applied to health monitoring of wind turbine drivetrain components Wind Energy. DOI: 10.1002/We.1512 |
0.328 |
|
2010 |
Sheng S, Gao RX. Multi-time scale modeling strategy for bearing life prognosis Proceedings of the Asme Dynamic Systems and Control Conference 2009, Dscc2009. 645-652. DOI: 10.1115/DSCC2009-2680 |
0.416 |
|
2007 |
Gao RX, Sheng S. Nondestructive testing for bearing condition monitoring and health diagnosis Ultrasonic and Advanced Methods For Nondestructive Testing and Material Characterization. 439-470. DOI: 10.1142/9789812770943_0019 |
0.463 |
|
2006 |
Sheng S, Zhang L, Gao RX. A systematic sensor-placement strategy for enhanced defect detection in rolling bearings Ieee Sensors Journal. 6: 1346-1354. DOI: 10.1109/Jsen.2006.881421 |
0.548 |
|
2006 |
Sheng S, Gao RX. Optimization of ANFIS with applications in machine defect severity classification Ieee International Conference On Neural Networks - Conference Proceedings. 728-734. |
0.453 |
|
2005 |
Gao RX, Wang C, Sheng S. Sensor placement strategy for high quality sensing in machine health monitoring Smart Structures and Systems. 1: 121-140. DOI: 10.12989/Sss.2005.1.2.121 |
0.603 |
|
2004 |
Sheng S, Gao RX. Architectural effect on anfis for machine condition assessment American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) Dsc. 73: 663-670. DOI: 10.1115/IMECE2004-60071 |
0.473 |
|
2004 |
Sheng S, Gao RX. Structural dynamics-based sensor placement strategy for high quality sensing Proceedings of Ieee Sensors. 2: 642-645. |
0.507 |
|
2003 |
Sheng S, Gao RX. A wavelet-based fuzzy data fusion scheme for bearing defect severity classification American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) Dsc. 72: 1285-1292. DOI: 10.1115/IMECE2003-42584 |
0.44 |
|
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