STFT和HHT在风力机轴承故障诊断中的应用

Application of Short Time Fourier Transform and Hilbert-Huang Transform in Fault Diagnosis of Rolling Bearings of Windmill

对异常工作风力机进行振动测试,获取振动测试信号,选取额定风速时采集的一段信号分别进行时频域分析、短时傅里叶变换分析、希尔伯特黄变换分析,提取故障特征信号。结果表明,短时傅里叶变换具有较好的时频分辨率,能够较好的提取故障特征信号,较为适合非线性、非平稳信号的分析;希尔伯特黄变换是一种很好的自适应信号处理方法,非常适合于非线性和非平稳信号的分析。希尔伯特黄变换对于风力机滚动轴承的故障诊断取得了很好的效果,准确提取了故障信号特征。对风力机故障诊断技术的发展具有较大的意义。

The vibration signals of wind turbines in abnormal operation were acquired through the vibration testing.The signals collected at the rated wind-speed were selected for time-frequency domain analysis,short-time Fourier transform（STFT） analysis and Hilbert-Huang transform（HHT） analysis.Then,the fault feature signals were extracted.The results show that the STFT has a good time-frequency identification rate,and can extract the fault feature signals well;while the HHT is a good method for adaptive signal processing,and very suitable for nonlinear and non-stationary signal analysis.The HHT is effective in fault diagnosis of rolling element bearings for wind turbines,and it has important significance for the development of fault diagnosis technique for wind turbines.