Applied Mechanics and Materials
Vol. 862
Vol. 862
Applied Mechanics and Materials
Vol. 861
Vol. 861
Applied Mechanics and Materials
Vol. 860
Vol. 860
Applied Mechanics and Materials
Vol. 859
Vol. 859
Applied Mechanics and Materials
Vol. 858
Vol. 858
Applied Mechanics and Materials
Vol. 857
Vol. 857
Applied Mechanics and Materials
Vol. 856
Vol. 856
Applied Mechanics and Materials
Vol. 855
Vol. 855
Applied Mechanics and Materials
Vol. 854
Vol. 854
Applied Mechanics and Materials
Vol. 853
Vol. 853
Applied Mechanics and Materials
Vol. 852
Vol. 852
Applied Mechanics and Materials
Vol. 851
Vol. 851
Applied Mechanics and Materials
Vol. 850
Vol. 850
Applied Mechanics and Materials Vol. 856
Paper Title Page
Abstract: Pressure regulators have a great impact on the runtime and energy savings of supersonic wind tunnels. In order to investigate this impact quantitatively the theoretical isentropic (ideal) equations for the supersonic wind tunnel flow were compared with viscous CFD computations. The theoretical model was extended include the flow from the outflow from the pressure vessel up to the wind tunnel. This theoretical model was solved with and without pressure regulation valve using a Runge-Kutta method. The differences in runtime and energy consumption for both configurations with and without pressure regulator as well as the derivation of the analytical model and the numerical solution are presented in detail in this paper.
238
Abstract: A high availability of machines has always been important in production. One way to increase it is to avoid unscheduled production stops by detecting the onset of machine faults and to conduct preventative repairs. The detection part consists of the three steps signal acquisition, feature extraction and classification. This paper focuses on the last two steps through the example of an induction motor. Based on a publicly available motor current data set, features were extracted using the continuous wavelet transform. In the subsequent classification step eight different classification methods were compared with each other. It was found, that the accuracy of the classifiers varied significantly in a range from 20.6 % to 92.8 %. Moreover, the supportive vector machine, scoring an accuracy of 92.8 %, was the only classifier with an accuracy above 55.0 %.
244