Frictional effects on the dynamic responses of gear systems and the diagnostics of tooth breakages

To develop accurate diagnostic techniques, this study examines the dynamic responses of spur gear transmission system with including frictional effects on a tooth mesh process. An 8-degree-of-freedom model is developed to include the effects of supporting bearings, a driving motor and a loading system. Moreover, it takes into account not only the time-varying stiffness, but also the time-varying forces and moments due to the frictional effect. The latter causes additional vibration responses in the direction of the off-line-of-action (OLOA). To show the quantitative effect of the friction, vibration responses are simulated under different friction coefficients. It shows that an increase in friction coefficient value causes a nearly linear increase in the vibration features of diagnostics. However, features from torsional responses and the principal responses in the line-of-action show less changes in the vibration level, whereas the most significant increasing is in the OLOA direction. Furthermore, the spectral peaks at the rotational and sideband frequencies are influenced significantly by small breakage defects, especially when the friction effect is taken into account. In addition, the second and third harmonics of the mesh frequency are more influenced than the first harmonic component for all motions, which can be effective features for both indicating lubrication deterioration and improving conventional diagnostic features

Isothermal cross-sections of Gd-Fe-Ge system at 800 °C and magnetic properties of Gd117Fe52Ge112 compound

International audienceThe isothermal section of the phase diagram of the Gd-Fe-Ge ternary system at 800 °C was investigated by X-ray powder diffraction and scanning electron microscope-energy dispersive X-ray spectroscopy. Three intermediate solid solutions have been identified or confirmed: one lacunaire GdFexGe2 (CeNiSi2-type of structure space group P6/mmm) and two by substitition GdFe4-xGex (AlB4-type of structure space group I4/mmm) and GdFe12-xGex (YCo6Ge6-type of structure space group P6/mmm) and one ternary compound Gd117Fe52Ge112 (Tb117Fe52Ge112-type of structure space group Fm-3m). The magnetic properties of Gd117Fe52Ge112 compound is ferromanetic at 89 K have been studied. The 800 °C phase diagram of this system consists of 20 three-phases regions

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

International audienceThe resolution enhancement of textual images poses a significant challenge mainly in the presence of noise. The inherent difficulties are twofold. First is the reconstruction of an upscaled version of the input low-resolution image without amplifying the effect of noise. Second is the achievement of an improved visual image quality and a better OCR accuracy. Classically, the issue is addressed by the application of a denoising step used as a preprocessing or a post-processing to the magnification process. Starting by a denoising process could be more promising to avoid any magnified artifacts while proceeding otherwise. However, the state of the art underlines the limitations of denoising approaches faced with the low spatial resolution of textual images. Recently, sparse coding has attracted increasing interest due to its effectiveness in different reconstruction tasks. This study proves that the application of an efficient sparse coding-based denoising process followed by the magnification process can achieve good restoration results even if the input image is highly noisy. The main specificities of the proposed sparse coding-based framework are: (1) cascading denoising and magnification of each image patch, (2) the use of sparsity stemmed from the non-local self-similarity given in textual images and (3) the use of dual dictionary learning involving both online and offline dictionaries that are selected adaptively for each local region of the input degraded image to recover its corresponding noise-free high-resolution version. Extensive experiments on synthetic and real low-resolution noisy textual images are carried out to validate visually and quantitatively the effectiveness of the proposed system. Promising results, in terms of image visual quality as well as character recognition rates, are achieved when compared it with the state-of-the-art approaches