基于CL3-AMW与LTSA的水电机组振动故障特征提取研究

Study on extraction of vibration fault features of hydro-turbine generating units based on CL3-AMW and LTSA

水电机组结构复杂,运行环境恶劣,其运行状态受水、机、电多因素耦合影响,使得传统特征提取方法所得特征敏感性差。针对此问题,提出了基于CL3自适应多小波(CL3 adaptive multiwavelets,CL3-AMW)与局部切空间排列(local tangent space alignment,LTSA)的水电机组振动故障特征提取方法。该方法根据水电机组振动信号特点,对信号处理方法进行自适应改变,通过高维振动故障特征集的自适应构建、特征选择和特征融合三个方面提高水电机组振动故障特征敏感性。利用该方法进行特征提取实验,结果表明,与其他方法相比,所提出的方法能够提高故障特征参数敏感性,为准确识别机组振动故障奠定基础。

With complicated structure and severe operating environment,hydro-turbine generating units are often affected by hydraulic,mechanical and electric coupling factors,which degenerates the sensitivity of vibration fault features extracted using traditional methods.To overcome this problem,we present a new vibration feature extraction method based on CL3 adaptive multiwavelets(CL3-AMW)and local tangent space alignment(LTSA).This method improves the sensitivity through a three-step procedure:high dimensional vibrant feature set adaptive construction,feature selection and feature fusion.It has been applied to vibration signals collected from a rotating machinery system and hydro-turbine generating units,and compared with other methods.The results show the sensitivity of vibration fault features extracted by our method are more sensitive than those by other methods,thus providing useful data for more accurate fault diagnosis of hydro-turbine generating units.