故障转子系统轴心轨迹的自动识别研究

轴心轨迹是转子系统故障诊断的重要依据,将整周期重采样、归一化的极半径序列引入轴心轨迹自动识别系统。首先对振动信号进行整周期重采样以降低转速和采样频率对小波去噪效果的影响,然后利用小波变换对其去噪并合成提纯的轴心轨迹,最后计算具有平移、伸缩和旋转不变性的极半径序列作为轴心轨迹特征,采用BP神经网络进行识别。实验结果表明该方法具有良好的识别效果。

Automatic identification of orbits of rotor system with faults

Rotor orbit is an important basis for rotor system fault diagnosis. Full period re sampling and normalized polar radius sequence are introduced into the automatic recognition system. Firstly, vibration signals are processed with full period resampling to reduce the influence on the wavelet de-noising effect by rotation speed and sampling frequency. Then, they are de-noised with wavelet transform to synthetic purified orbit. Lastly, normalized polar radius sequence which is invariant to translation, scaling and rotation of the rotor orbit is calculated and used as the orbit feature. Rotor orbits are identified using the BP neural network. Experimental results show that the method has good recognition effect.