一种聚类优化融合故障诊断方法及其应用

中国机械工程

一种聚类优化融合故障诊断方法及其应用

蒋玲莉1,2;莫志军1;陈安华1;李学军1

1.湖南科技大学机械设备健康维护湖南省重点实验室，湘潭，411201
2.苏州东陵振动试验仪器有限公司，苏州，215163

出版日期:2016-08-10 发布日期:2016-08-10
基金资助:
国家自然科学基金资助项目（51575177）；湖南省教育厅优秀青年项目(14B057) ；湖南省教育厅资助重点项目（13A023）

Clustering Optimization Fusion Method for Fault Diagnosis and Its Applications

Jiang Lingli1,2;Mo Zhijun1;Chen Anhua1;Li Xuejun1

1.Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan, Hunan, 411201
2. Suzhou Dongling Vibration Test Instrument Limited Company, Suzhou, Jiangsu, 215163

Online:2016-08-10 Published:2016-08-10
Supported by:

摘要/Abstract

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。

关键词: 聚类分析, 权值矩阵, 融合诊断, 遗传算法

Abstract: 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 Kmeans 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 algorithm was used to optimize the weight matrix. The examples of bearing fault diagnosis show that the clustering optimization fusion method may improve diagnostic accuracy.

Key words: clustering analysis, weight matrix, fusion diagnosis, genetic algorithm

中图分类号:

TP206.3
TH17

蒋玲莉, 莫志军, 陈安华, 李学军. 一种聚类优化融合故障诊断方法及其应用[J]. 中国机械工程.

Jiang Lingli, Mo Zhijun, Chen Anhua, Li Xuejun. Clustering Optimization Fusion Method for Fault Diagnosis and Its Applications[J]. China Mechanical Engineering.

参考文献

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