This paper describes an innovative free-form modeling system, Virtual Clay Modeling System (VCMS), in which users can directly manipulate the shape of a virtual object like a clay model in real world. With this system, some disadvantages of interaction with computer aided industry design (CAID) systems can be resolved. In order to enhance the immersion feelings and improve the controlling abilities to cut, paste, and compensate of VCMS, we use Spaceball 5000 and PHANTOM Desktop to assign the set of interaction tasks. During the process of realizing 6 degree-of-freedom (DOF) haptic feedback modeling control, we developed and accomplished the device interfaces with Open Inventor and Qt application framework. VCMS provides us a good immersion of allowing for effective modeling in a virtual world.
The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.
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