基于混合时频分析方法的风电机组故障诊断

针对风电机组传动链系统振动信号非高斯、非平稳性的特点,提出了一种基于混合时频分析的风电机组故障诊断方法。该方法首先采用参数优化Morlet小波消噪方法对原始振动信号进行分析,滤除强大的背景噪声干扰;进而通过自项窗方法抑制时频面的干扰项,增强信号特征成分,提取故障特征以实现故障诊断。在Morlet小波参数优化过程中,采用交叉验证法优化波形参数及连续小波变换的尺度参数;在自项窗的设计过程中,采用基于平滑伪魏格纳分布的函数进行设计,并通过两次阈值处理以减少运算量、提高运算效率。通过对风电机组监测振动数据分析,证明了该方法可以有效地实现背景噪声的消除和故障诊断。

Wind Turbine Fault Diagnosis Based on the Hybrid Time-frequency Analysis Method

Aiming at the non-Gaussian and non-stationary characteristics of the wind turbine vibration signals,this paper proposed a new fault diagnosis method based on the hybrid time-frequency analysis.This new method dealed with the raw vibration signals by the parameter optimizied Morlet wavelet de-noising method,to filter the strong background noise interruption.Then the auto term window method was introduced to suppress the cross terms in the time-frequency domain,to strengthen the useful fault features,to extract the fault features and to realize the diagnosis.In the parameter optimiziation process,the cross validation method was introduced to optimize the Morlet wavelet parameters and the scale parameter.The auto term function is designed based on the smooth pseudo Wigner-Ville distribution(SPWVD) and two threshold processes were introduced to reduce the computation and enhance the computing efficiency.The wind turbine vibration signal analysis proved that this new method can not only suppress the noise interruption in the background,but also realize the fault diagnosis efficiently.