Xiang Ling, Su Hao, Li Ying
School of Mechanical Engineering, North China Electric Power University, Baoding 071003, China.
Entropy (Basel). 2020 Jun 18;22(6):682. doi: 10.3390/e22060682.
Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and the 1.5-dimensional Teager kurtosis spectrum. The composite fault signal was deconvoluted using the MOMEDA method. The deconvoluted signal was analyzed by applying the 1.5-dimensional Teager kurtosis spectrum. Finally, the frequency characteristics were extracted for the bearing fault. A bearing composite fault signal with strong background noise was utilized to prove the validity of the method. Two actual cases on bearing fault detection were analyzed with wind turbines. The results show that the method is suitable for the diagnosis of wind turbine compound faults and can be applied to research on the health behavior of wind turbines.
风力涡轮机在强烈的背景噪声中运行，且多个故障常常发生在特征相互混合且容易被误判的地方。为了从风力涡轮机中提取滚动轴承的复合故障，提出了一种基于多点最优最小熵反卷积调整（MOMEDA）和1.5维Teager峭度谱的新型混合方法。使用MOMEDA方法对复合故障信号进行反卷积。通过应用1.5维Teager峭度谱对反卷积后的信号进行分析。最后，提取轴承故障的频率特征。利用一个具有强背景噪声的轴承复合故障信号来证明该方法的有效性。对两个风力涡轮机轴承故障检测的实际案例进行了分析。结果表明，该方法适用于风力涡轮机复合故障的诊断，可应用于风力涡轮机健康行为的研究。
