基于模糊核聚类和引力搜索的风电齿轮箱故障诊断

为了诊断风电齿轮箱已知类别和未知类别的故障，提出了基于模糊核聚类和引力搜索的故障诊断方法。首先建立以训练样本分类错误率为目标的聚类模型，利用模糊核聚类对训练样本进行分类；然后利用引力搜索算法求解聚类模型，获得最优分类结果下每个类的类心；最后根据新样本与各类心之间的核空间样本相似度判断属于已知故障或者未知故障。结果表明，该方法准确度高，可有效用于风电齿轮箱故障诊断。

Fault Diagnosis of Wind Turbine Gearbox Based on Kernel Fuzzy C-means Clustering and Gravitational Search

In order to diagnose known faults and unknown faults of wind turbine gearbox, a method was proposed based on kernel fuzzy c-means clustering and gravitational search. Firstly, the clustering model was built based on wrong classification rate of training samples. The training samples were classified by kernel fuzzy c-means clustering. Then the gravitational search method was introduced for solving the clustering model. The class centers of optimal clustering result were acquired. Finally, the similarity parameters in kernel space between new data samples and the class centers were calculated for diagnosing whether the new data sample belonged to the known faults. The results show that the proposed method has higher precision, which can be applied to diagnose fault of wind turbine gearbox.