改进希尔伯特–黄变换方法提取水轮机动态特征信息

Dynamic Characteristic Information Extraction of Hydroturbine Based on Improved Hilbert-Huang Transform Method

常规的希尔伯特–黄变换(Hilbert-Huang transform,HHT)方法能较好地分析水轮机轴系信号,但经经验模态(empirical mode decomposition,EMD)分解后,在原始信号的低频区易产生虚假的内禀模态函数(intrinsic mode function,IMF)分量,干扰特征信息的提取,引发误判。该文提出基于能量波动的改进HHT方法及其判别条件。该方法利用分量信号能量递减原则并设定判别阈值来跟踪筛选虚假分量。通过仿真信号对该方法进行了有效性验证,并以原型水轮机非最优工况下动态信号为例,进行了应用检验。结果表明,该方法具有良好的虚假分量识别能力,提取真实的水轮机特征参量,更加适合分析复杂而特殊的水轮机动态特征信息。

The conventional Hilbert-Huang transform（HHT） method can analyze hydroturbine shafting signals well.However,after the empirical mode decomposition,in the low frequency zone of the original signals,illusive intrinsic mode function（IMF） component can be easily generated.This would interfere with the extraction of characteristic information,resulting in erroneous judgment.An improved HHT method and its discrimination conditions on the basis of energy fluctuation were advanced.The principle of energy degradation for component signals was applied in this method.Discrimination threshold was set to track and screen the illusive components.The validity of this method was validated by simulation signals,and practical application was carried out with an example of dynamic signals of prototype hydroturbine on a non-optimal operating condition.Results show that,compared with the conventional method,this method can identify illusive components well and extract true characteristic parameters of hydroturbine.In addition,this method is more appropriate for the analysis of complex and specific dynamic characteristic information of hydroturbine.