The use of signal processing for condition monitoring of wind turbines data has been on-going since several decades. Failure in the analysis of high modulated data may make the machine break. An example of this is the reported real case of bearing failure on a Repower wind turbine, which could not be detected by currently applied methods. The machine had to be out of service immediately after a faulty bearing outer race was visually ascertained. Vibration dataset from this faulty machine was provided to facilitate research into wind turbines analysis and with the hope that the authors of this work can improve upon the existing techniques. In the response to this challenge, the authors of this paper proposed Spectral Kurtosis (SK) and Signal Intensity Estimator (SIE) as proven time-frequency fault indicators to tackle the question of data with different modulation rates. Extensive signal processing using time domain and time-frequency domain analysis was undertaken. It was concluded that SIE is well established mature approach and it provides a more reliable estimate of wind turbine conditions than conventional techniques such as SK, leading to better discrimination between “good” and “bad” machines.

几十年来，一直在使用信号处理来监视风力涡轮机数据。高调制数据分析失败可能会导致机器损坏。举一个例子，据报道是Repower风力涡轮机轴承出现故障的实际情况，当前应用的方法无法检测到这种情况。在目视检查出有故障的轴承外圈之后，必须立即停止使用该机器。提供了该故障机器的振动数据集，以促进对风力涡轮机分析的研究，并希望这项工作的作者可以改进现有技术。为了应对这一挑战，本文的作者提出了频谱峰度（SK）和信号强度估计器（SIE）作为行之有效的时频故障指标，以解决具有不同调制率的数据问题。使用时域和时频域分析进行了广泛的信号处理。得出的结论是，SIE是成熟的成熟方法，并且与传统技术（例如SK）相比，它提供了更可靠的风轮机状态估计值，从而更好地区分了“好”和“坏”的机器。