To reduce the influence of the static unbalance on an infrared missile guidance system, a new static unbalance measure system for the gimbals axes has been developed. Considering the coupling effects caused by a mass eccentricity, the static balance condition and measure sequence for each gimbal axis are obtained. A novel static unbalance test approach is proposed after analyzing the dynamic model of the measured gimbal axis. This approach is to drive the measured gimbal axis to do sinusoidal reciprocating motion in a small angle and collect its drive currents in real time. Then the static unbalance of the measured gimbal axis can be obtained by the current multi-cycle integration. Also a measuring system using the proposed approach has been developed. A balanced simulator is used to verify the proposed approach by the load and repeatability tests. The results show the proposed approach enhances the efficiency of the static unbalance measurement, and the developed measuring system is able to achieve a high precision with a greater stability.

In order to address the difficult problem of ball mill load identification during milling operation, the multi-scale fuzzy entropy algorithm is introduced into ball mill load identification and an innovative ball mill load identification method is proposed- the complete integrated empirical decomposition based on adaptive noise (CEEMDAN)-joint denoising with wavelet thresholding-multi-scale fuzzy entropy biased mean value (PMMFE) ball mill load identification method. Firstly, the vibration signals of ball mill bearings are denoised by the CEEMDAN-wavelet threshold joint denoising method and the analysis reveals that this method has obvious advantages over other denoising methods; secondly, the fuzzy entropy, multi-scale fuzzy entropy, and multi-scale fuzzy entropy deviation of denoised vibration signals are computed, the relationship between each entropy feature and the mill load is analysed in-depth and in an information-rich manner. Finally, the least squares support vector algorithm is used to identify the load of the feature vector. The analysis of the measured vibration signals reveals that the overall recognition rate of this method is 84.4%, which is significantly higher than that of other denoising methods and the combination of feature parameters, and the experiments show that the mill load recognition method based on CEEMDAN-wavelet thresholding-PMMFE is able to effectively identify the different loading states of ball mills.