基于二值双谱和模糊聚类的风电轴承故障诊断

针对风电机组滚动轴承振动信号具有强噪声、非高斯、非线性及非平稳的特性,导致滚动轴承故障状态及故障位置难以确定的问题,提出了基于二值双谱和模糊聚类的风力发电机组滚动轴承故障诊断方法。首先,对振动信号进行双谱分析,获得其二值双谱特征;其次,以基于目标函数的模糊聚类方法,构造各类故障的目标模板;最后,按照最邻近准则设计分类器,以目标模板与测试样本之间的距离测度作为模式分类依据,对风电机组滚动轴承的故障位置进行判断。实验结果表明,该方法能有效诊断故障状态及故障位置,其诊断准确性高、稳定性好、计算量小、速度快,且以距离测度为故障判决依据,使诊断结果易于理解和解释、便于检验。

Bearing Fault Pattern Recognition of Wind Turbine Based on Two-Value Bispectrum Feature-Fuzzy Clustering Method

It is difficult to identify the fault state and location of the roller bearings of a wind turbine due to the complexity of its vibration signals. They suffer strong interference and are non-Gaussian, nonlinear and non-stationary. In the light of these characteristics, a fault diagnosis method is put forward based on the binary-bispectrum and fuzzy clustering. First, the binary-bispectrum features are obtained. Then, the target templates for different faults are constructed based on fuzzy clustering method. Finally, the proximity classifier is designed to determine the roller bearing fault position according to the distance between the test samples and corresponding target templates. The experimental results show that this method can effectively diagnose the fault state and fault location with high accuracy, good stability, less calculation and fast speed. Furthermore, the results is easy to understand and verify when the relative distance is taken as the basis to diagnose the faults.