transmission pair
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2021 ◽  
pp. 107754632110310
Author(s):  
Rukang Hu ◽  
Guangwu Zhou ◽  
Junyang Li

The torsional vibration problems often occur to industrial robots during start and stop due to the use of flexible wheel in harmonic gear reducer as a transmission pair. Because of the influence of nonlinear stiffness, transmission error, and other factors, the nonlinear torsional vibration of harmonic gear reducer will affect the stability and reliability of transmission system. In this article, based on the structural characteristics of the harmonic gear reducer, considering the nonlinear torsional stiffness, transmission error, meshing damping, and other factors, the nonlinear torsional dynamic model of the harmonic gear reducer was established with the theoretical analysis and the experimental results. Based on this model, the influence of various factors such as rotational speed, moment of inertia, torsional stiffness, and transmission error on the nonlinear torsional vibration of harmonic gear reducer was discussed. The results show that the vibration amplitude of harmonic gear reducer increases with the increase of speed, transmission error, and motor inertia, and decreases with the increase of load inertia and damping. The dynamic model and analysis method established in the study can provide the theoretical guidance for the optimal design and the vibration reduction of harmonic gear reducer.


Retrovirology ◽  
2015 ◽  
Vol 12 (1) ◽  
Author(s):  
Jacqui Brener ◽  
Astrid Gall ◽  
Rebecca Batorsky ◽  
Lynn Riddell ◽  
Soren Buus ◽  
...  

2007 ◽  
Vol 195 (9) ◽  
pp. 1239-1241 ◽  
Author(s):  
Frederick M. Hecht ◽  
Leslie E. Wolf ◽  
Bernard Lo

2002 ◽  
Vol 3 (10) ◽  
pp. 1058-1072 ◽  
Author(s):  
William Roth ◽  
Mafhuz Khan ◽  
Romas Geleziunas ◽  
Harold Stringer ◽  
Jalal Zuberi ◽  
...  

Author(s):  
Sidi M. Berri ◽  
J. M. Klosner

Abstract This paper investigates a new strategy for early detection of defects in a power transmission pair of spur gears. Sensitivity to local defects is enhanced by processing the signal as follows. The orthogonal discrete wavelet transform (ODWT) of the band-pass filtered averaged signal is first obtained. This is followed by thresholding in the wavelet domain, thereby removing the low amplitude noise contribution. The inverse wavelet transform then essentially reconstructs the component of the signal that is due to the defect. Experimental results demonstrate the efficiency of this procedure.


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