基于粒子群优化KFCM的风电齿轮箱故障诊断
Fault Diagnosis of Wind Turbine Gearbox Based on KFCM Optimized by Particle Swarm Optimization

针对基于有监督学习的方法法识别未知类别故障，提出了一种基于粒子群优化模糊核聚类（kernel fuzzy c means clustering，简称KFCM）的风电机组齿轮箱故障诊断方法。首先,建立以训练样本分类错误率为目标的聚类模型，利用KFCM对训练样本进行分类；然后,以初始聚类中心和核函数参数作为优化变量，利用粒子群优化算法求解聚类模型，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新样本属于已知故障或者未知故障。以某风电机组齿轮箱为例，对提出方法的有效性进行试验验证。结果表明，与传统基于有监督学习的神经网络方法相比，该方法能有效诊断已知和未知类别的故障。
A method based on kernel fuzzy c-means clustering (KFCM) optimized by particle swarm optimization is proposed for fault diagnosis of wind turbine gearbox. Firstly, the clustering model is built based on wrong classification rate of training samples. The training samples are classified by kernel fuzzy c-means clustering. Then particle swarm optimization is introduced for solving the clustering model while the initial clustering center and parameter of kernel function are chosen as optimization variables. The class centers of optimal clustering result are acquired. Finally, the similarity parameters in kernel space between new data samples and the class centers are calculated for diagnosing whether the new data sample belongs to knows faults. The results show that the proposed method can diagnose both the known faults and unknown faults effectively compared to traditional neural network based on supervised learning.