中文摘要：

针对单一聚类诊断方法难以准确、全面识别不同故障状态的问题,提出了一种聚类优化融合故障诊断方法。分别利用社团聚类、K-均值聚类及粒子群聚类三种方法对故障进行识别,得出三种聚类方法对应的故障识别准确率,在此基础上构建初始权值矩阵,并通过遗传算法对初始判断矩阵与三种聚类方法进行优化,得到最优权值矩阵与优化的聚类模型,用于融合诊断。轴承故障诊断实例结果表明,该聚类融合诊断方法能够有效提高故障识别准确率。

英文摘要：

Single community diagnosis clustering methods were difficult to identify different fault states, in order to improve diagnostic accuracy, a fusion clustering method was proposed herein based on genetic optimization algorithm. Three clustering methods, the community clustering, the K-means clustering and the particle swarm clustering, were used to identify the fault states respectively. The diagnostic accuracies were used to construct an initial weight matrix. The genetic optimization algo- rithm was used to optimize the weight matrix. The examples of bearing fault diagnosis show that the clustering optimization fusion method may improve diagnostic accuracy.