基于TSHI的LMD振动传感信号特征量提取方法

LMD method based on TSHI for vibration sensor signal feature extraction

针对复杂、非平稳振动传感信号特征量提取的需求,研究一种基于三角Shepard的Hermite插值改进算法TSHI,并提出一种基于TSHI的LMD振动传感信号特征量提取方法。TSHI将二元Hermite插值函数和密切三角Shepard基函数相结合,构建复杂、散乱振动传感信号的包络曲线插值多项式,该算法在插值过程可根据插值点与所在三角形各顶点的时间距离调整局部插值曲线,使包络估计曲线更合理。基于TSHI的LMD方法将振动传感信号分解为若干个包含时频特征尺度的乘积函数分量PF分量,再将各主要PF分量的能量组合、构成信号特征量向量。试验结果表明,TSHI对复杂、高频率振动传感信号的包络曲线插值可避免相位差、过包络和欠包络等问题,插值结果RMSE小;应用基于TSHI的LMD方法的相关向量机RVM故障诊断模型对振动传感器各种状态的诊断正确率接近100%。

For the feature extraction requirements of complex and non-stationary vibration sensor signal, this paper researches Hermite interpolation based on triangular Shepard(TSHI) and LMD method based on TSHI for vibration sensor signal feature extraction. TSHI combines binary Hermite interpolation functions with osculatory triangular Shepard basis functions. Then it creates the envelope curve interpolation polynomial of complex and scattered vibration sensor signal. It can adjust the local interpolation curve according to the time distance between the interpolation point and every vertex of the triangle, so that the envelope estimation curve is more suitable. The LMD method based on TSHI decomposes the vibration sensor signal into several product functions(PF) which has time-frequency characteristic scale of the signal. Then the method combines the energy of the main PFs to form the signal characteristic vector. The experimental results show that TSHI for complex and high frequency vibration sensor signal’s envelope curve can avoid phase difference, over envelope and under envelope. The RMSE of the TSHI result is smaller. If relevance vector machine(RVM) fault diagnosis model applies the LMD method based on TSHI, its diagnostic accuracy of vibration sensor is close to 100%.