基于群智能加权核聚类的水电机组故障诊断
Fault Diagnosis for Hydroelectric Generator Unit Based on Electromagnetism-Like Artificial Bee Colony Weighted Kernel Clustering

针对核聚类中核参数选择依赖经验，最优聚类中心难以有效获取的问题，提出了一种仿电磁蜂群加权核聚类算法。首先，考虑不同特征对聚类结果的影响，对样本进行加权处理，利用核空间的Xie-Beni指标建立加权核聚类模型；然后，提出并引入仿电磁蜂群算法求解聚类模型，实现聚类中心、特征权重与核参数的同步寻优。利用该方法分别对3组标准测试样本集以及水电机组故障样本进行聚类测试，并与传统方法进行对比分析。试验结果表明，提出的仿电磁蜂群加权核聚类算法较传统聚类方法具有更高的精度，能够有效实现水电机组振动故障的准确聚类与识别，完成故障诊断。
In the fault diagnosis of a hydro-turbine generating unit (HGU), kernel clustering is a valid non-supervised learning method. In order to solve the problems of kernel parameter selection and cluster center calculation, a novel electromagnetism-like artificial bee colony weighted kernel clustering (EAWKC) is proposed. First, after considering the influence of different symptoms, the data is weighted, and the clustering model is built based on the kernel Xie-Beni clustering index. Then, the electromagnetism-like artificial bee colony (ELABC) method is proposed and introduced in order to solve the objective function to realize the synchronized optimization of the clustering center, symptom weight and kernel parameter. The classification accuracy of EAWKC is checked by three of the UCI testing data sets and the HGU fault samples, and compared with the traditional method. The experimental results show that EAWKC has higher accuracy and can effectively complete the fault diagnosis.