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针对LabVIEW中缺少经验模态分解(EMD)算法模块的问题,对LabVIEW进行了二次开发,建立了基于LabVIEW的EMD模块,为振动故障信号分析提供了有效的工具,进而以水轮机故障信号的振动特征和故障产生机理为依据,将此算法运用于水轮机主轴振动信号分析,以河北省西达水电站水轮机主轴振动数据为基本资料,对分解得到的高频本征模函数(IMF)分量做包络谱分析,提取故障信息,并与轴心轨迹分析方法相结合加以验证。结果表明,该方法能够有效判别出水轮机主轴故障类型,可应用于水轮机主轴振动信号分析。
In view of the lack of empirical mode decomposition (EMD) algorithm module in LabVIEW, this paper makes a second development of LabVIEW and builds an EMD module based on LabVIEW, which provides an effective tool for vibration fault signal analysis. Then, Characteristics and fault generation mechanism, this algorithm is applied to the vibration signal analysis of turbine main shaft. Based on the vibration data of turbine main shaft of Xida Hydropower Station in Hebei Province as the basic data, the decomposed high frequency intrinsic mode function (IMF) components are enveloped Spectrum analysis, extraction of fault information, and with the combination of axis trajectory analysis method to be verified. The results show that this method can effectively identify the fault type of the main shaft of the turbine and can be applied to the vibration signal analysis of the turbine main shaft.