| 1. | Purification of rotor center ' s orbit with mathematical morphology filters
采用数学形态滤波器的轴心轨迹提纯 |
| 2. | Stick to concept of scientifical development , construct harmonious jing - tang port area
基于不变矩特征和新型关联度的轴心轨迹形状自动识别 |
| 3. | Diagnosis of sliding bearing based on quadrantal area information entropy of axle centre track
基于轴心轨迹象限面积信息熵滑动轴承的诊断 |
| 4. | Dynamic simulation on track of main shaft axis of radial sliding bearing submitting dynamic pressure
径向动压滑动轴承主轴轴心轨迹的动态仿真 |
| 5. | Afterwards , neural network linked the non - linear relationship between the track ’ s parameters and it ’ s characteristics
选择改进型bp网络,经过学习和优化,建立轴心轨迹参数和特征谱之间的非线性映射。 |
| 6. | At present among commonly used diagnosis methods , it is practical to diagnose axial vibration fault with frequency analysis and axis locus analysis
在目前企业中较常用的振动分析诊断中，利用频谱分析与轴心轨迹分析诊断轴系振动故障，具有实际意义。 |
| 7. | The moving trace of axes for sliding bearings under dynamic loading is demonstrated with a qualitative method so that the students can have the acquaintance for it
对于动载滑动轴承的轴心运动轨迹，采用定性演示的办法，使学生对轴心轨迹有一个感性认识。 |
| 8. | To this end , the time - base diagram , orbit and fft are used to evaluate the vibration signals measured on the machine . only under necessary condition is the balancing of a rotor carried out
为此，我们采用了时域分析、轴心轨迹和频谱分析( fft )等方法，对转子上测得的振动信号进行处理、分析。 |
| 9. | In this paper , we combine normalized pattern spectrum , relative mome - nts and geometrical characteristic with artificial neural network to achieve classify and recognize the chart of axes track
本文主要介绍了以归一化数学形态学谱、相对矩和形状参数为特征,结合人工神经网络实现对轴心轨迹的自动识别和分类。 |
