Narrow Results

In the present study a single degree of freedom oscillator with clearance type non-linearity is considered. Such oscillator represents the simplest model able to analyze a single teeth gear pair, neglecting: bearings and shafts stiffness and multi mesh interactions. One of the test cases considered in the present work represents an actual gear pair that is part of a gear box of an agricultural vehicle; such gear pair gave rise to noise problems. The main gear pair characteristics (mesh stiffness and inertia) are evaluated after an accurate geometrical modelling. The meshing stiffness of the gear pair is piecewise linear and time varying (in particular periodic); it is evaluated numerically using nonlinear finite element analysis (with contact mechanics) for different positions along one mesh cycle, then it is expanded in Fourier series. A direct numerical integration approach and a smoothing technique have been considered to obtain the dynamic scenario. Bifurcation diagrams of Poincaré maps are plotted according to some sample case study from literature. Optimization procedures are proposed, in order to find optimal involute modifications that reduce gears vibration.

The during the gear fault detection and diagnosis, the fault feature extraction is the key of diagnosis, but it is multifarious for the method of fault feature extraction. On the basis of continuous wavelet transform, this paper mainly introduces that the basic principle of wavelet transform and its application in feature extraction for fault diagnosis are set forth. It is aimed at no steady state pulse signal feature when transmission gear have fault, select suitable wavelet, apply wavelet transformation successfully to diagnose crackle fault of transmission gear, which indicates the advantage of wavelet analysis. The results show that the continuous wavelet transform is a useful tool in diagnosis of crack in gears. The new method for prognosis and diagnosis of incipient small crack can be applied to engineering practice effectively.

In order to overcome the shortcomings in the traditional wavelet analysis, a new approach based on Laplace wavelet transform (LWT) time frequency plot is proposed for detection and diagnosis defects in gearbox. The Laplace wavelet is a complex, analytical, and single sided damped exponential function and is self-adaptive to non-stationary and nonlinear signal, which can detect the singularity characteristic of a signal precisely under strong background noise condition. The basic principle is introduced in detail. The gear wear fault vibration signal is firstly decomposed using Laplace wavelet transform. In the end, the Laplace wavelet transform time frequency plot is obtained and the characteristics of the gear fault can be recognized according to the LWT time frequency spectrum. The experimental results show that the Laplace wavelet transform time frequency plot can effectively detect the gear fault.

In order to study the thermal equilibrium process of rocker arm's gear system, the flexible virtual prototype coupling model of shearer's rocker arm system was established, the load file outputted by dynamic simulation software ADAMS was loaded. The temperature-structure coupling analysis and fatigue life prediction of gear had been done by using the finite element software ANSYS, the temperature field nephogram and structure field nephogram of gear were obtained. Combining multi-field coupling technique and neural network could predict the safe working condition data of the low speed and heavy load gear of shearer, the error was 1.25e-5, providing a unequivocal basis for the design and optimization of gear type parts, which can improve the working reliability of this kind of parts effectively, this method also has instruction significance to shearer's practical productions.