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Titlebook: Rotating Machinery, Hybrid Test Methods, Vibro-Acoustics & Laser Vibrometry, Volume 8; Proceedings of the 3 Dario Di Maio,Paolo Castellini

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樓主: Odious
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發(fā)表于 2025-3-23 11:16:32 | 只看該作者
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發(fā)表于 2025-3-23 15:21:13 | 只看該作者
Nonlinear Phase Separation Testing of an Experimental Wing-Engine Structure,on of phase separation testing has been proposed for identifying nonlinear normal modes (NNMs) based on input and output measurements. The nonlinear phase separation (NPS) method integrates identification and continuation tools to calculate multiple NNMs from broadband data. The present contribution
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發(fā)表于 2025-3-23 18:54:08 | 只看該作者
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發(fā)表于 2025-3-24 01:23:06 | 只看該作者
Nonlinear 3D Dynamic Model of an Automotive Dual Mass Flywheel,g pistons. In standard operating conditions, the resulting torsional oscillations of the crankshaft are transferred to the gearbox, leading to a number of comfort problems. Rattle noise is one of these problems and it is caused by impulsive forces caused by clearances across the driveline and especi
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發(fā)表于 2025-3-24 05:29:59 | 只看該作者
Investigation of Notch-Type Damage Identification by Using a Continuously Scanning Laser Doppler Viter (CSLDV) system. The velocity response of a beam along a scan line under sinusoidal excitation is measured by the CSLDV system and a corresponding operating deflection shape (ODS) of the beam is obtained by the demodulation method. The ODS of the associated undamaged beam is obtained by using a p
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發(fā)表于 2025-3-24 08:46:38 | 只看該作者
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發(fā)表于 2025-3-24 11:11:58 | 只看該作者
Conference proceedings 2017e collection presents early findings and case studies on fundamental and applied aspects of Rotating Machinery, Hybrid Testing, Vibro-Acoustics & Laser Vibrometry, including papers on:.Rotating Machinery.Vibro-Acoustics.Experimental Techniques.Advances in Wind Energy.Scanning Laser Doppler Vibrometry Methods.Hybrid Test Methods.
18#
發(fā)表于 2025-3-24 16:19:34 | 只看該作者
Wind Turbine Health Monitoring: Current and Future Trends with an Active Learning Twist,ll try to address the motivation and the effectiveness of Structural Health Monitoring (SHM) machine learning applications for the different components of a WTs, as well as, the novel idea of intelligent WT in terms of data knowledge transfer and learning.
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發(fā)表于 2025-3-24 21:27:36 | 只看該作者
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發(fā)表于 2025-3-24 23:33:36 | 只看該作者
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