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Analysis the common gear fault signal, using continuous wavelet transform well time-frequency characteristics and failure mechanism gear features, combined with quasi-periodic signal rotary mechanical characteristics of the continuous wavelet transform fault signal. This method can reduce the noise source and other incentives interference, remove the specific needs of the signal to improve signal stripping effect, the use of a certain type of gear failures this step, to get a clear diagnosis results show that this method has proven a strong application space.

The preparation of biodiesel from Styrax Tonkinensis catalyzed by solid acid S2O82-/ZrO2-TiO2-Fe3O4 at an autoclave was studied in this paper. The magnetic catalysts were characterized by XRD, which explained the high catalytic effect. At the same time, the recovery rate and usage count of catalysts were also studied. And the results showed that a high yield of transesterification can be obtained in a closed autoclave at the condition of catalyst amount 5 %, reaction time 1.5 h, reaction temperature 373K and methanol and oil molar ratio 10:1. The results also showed that the catalysts were still with a higher catalytic efficiency when the catalysts were calcinated after the forth usage.

The occurrence of volatile arsenic in natural gas, could lead to the poisoning of catalyst in processing equipment, and pose a serious threat to human health. In order to detect the total volatile arsenic in natural gas, this paper analyzed the characteristic and original mechanism of the volatile arsenic first, then a novel method was carried out by using silver nitrate solution, saturated potassium persulfate solution and concentrated nitric acid solution as sorption solution, followed by volatile arsenic determination with ICP-MS .The result showed that 2% silver nitrate solution had a good adsorption effect to volatile arsenic, and the recoveries of arsenic ranged from 87%~103%, which basically met the demand for the rapid detection of volatile arsenic in natural gas.

In order to select the optimal location points for automated drilling and riveting process, an optimization method selecting the location points of Aircraft’s Wing Panel in Automatic Drilling and Riveting System is presented. This method includes three steps: 1) building the matching model based on the multiplicity; 2) presenting the matching method based on scatter search algorithm; 3) selecting the optimal location points according to the minimal deviation based on linear programming method. This method was applied in a panel to match the mathematical surface and the fitting one of this panel perfectly and select the optimal location points. The efficacy and quality during the process of drilling and riveting of this panel was improved greatly.