基于PCA与蚁群算法的机械故障聚类诊断方法

中国机械工程 ›› 2013, Vol. 24 ›› Issue (24): 3333-3337,3344.

基于PCA与蚁群算法的机械故障聚类诊断方法

陈安华;周博;张会福;潘阳

湖南科技大学,湘潭,411201

出版日期:2013-12-25 发布日期:2013-12-27
基金资助:
国家自然科学基金资助项目(51175169）；湖南省科技计划资助项目(2009FJ4055)；湖南省教育厅重点实验室开放基金资助项目(10K023)

Clustering Method of Mechanical Fault Diagnosis Based on PCA and Ant Colony Algorithm

Chen Anhua;Zhou Bo;Zhang Huifu;Pan Yang

Hunan University of Science and Technology,Xiangtan,Hunan,411201

Online:2013-12-25 Published:2013-12-27
Supported by:
National Natural Science Foundation of China（No. 51175169）;Hunan Provincial Science and Technology program ( No. 2009FJ4055)

摘要/Abstract

摘要：

针对现代机械复杂化、智能化的特点，为快速准确地诊断出设备故障，提出了基于PCA与蚁群算法的机械故障聚类诊断新方法。定义了聚类准确率判别因子,对主元的选取进行自适应调整，利用基于高斯径向基核函数的主元分析方法实现了故障特征提取。以蚁群算法解决旅行商问题为原型，定义了城市圈，改进蚁群算法实现了双重寻优，把故障聚类转化为蚁群算法最擅长的寻求最优解问题，将改进的蚁群算法用于故障特征样本的聚类。实例分析证明了该方法的有效性。

关键词: 主元分析, 蚁群算法, 聚类分析, 故障诊断

Abstract:

A new method of clustering for mechanical fault diagnosis based on PCA and ant colony algorithm was put forward for modern machinary because of the complexity and intelligence. A clustering accuracy discrimination factor was defined to adjust principle component. The mechanical fault feature extraction was realized based on Gauss RBF kernel function of the PCA. The fault clustering was transformed into find out optimal solution for the model of traveling salesman problem based on ant colony algorithm. The city circle was also defined to realize double optimization by ant colony algorithm. The improved ant colony algorithm was used for fault features of the sample clustering. The new method is effective by experiments.

Key words: principle component analysis(PCA), ant colony algorithm, cluster analysis, fault diagnosis

中图分类号:

TH39
TP391

陈安华, 周博, 张会福, 潘阳. 基于PCA与蚁群算法的机械故障聚类诊断方法[J]. 中国机械工程, 2013, 24(24): 3333-3337,3344.

Chen Anhua, Zhou Bo, Zhang Huifu, Pan Yang . Clustering Method of Mechanical Fault Diagnosis Based on PCA and Ant Colony Algorithm[J]. China Mechanical Engineering, 2013, 24(24): 3333-3337,3344.

参考文献

[1]杨昌昊, 竺长安, 胡小建. 基于贝叶斯网的复杂系统故障诊断方法[J]. 中国机械工程, 2009, 20(22): 2726-2732.
Yang Changhao, Zhu Changan, Hu Xiaojian. Methods on Complex System Fault Diagnosis Based on Bayesian Network[J]. China Mechanical Engineering, 2009, 20(22): 2726-2732.
[2]张冉, 赵成龙. 改进BP网络在汽轮发电机组故障诊断中的应用[J]. 计算机仿真, 2011, 28(7): 325-328.
Zhang Ran, Zhao Chenglong. Application of Improved BP Network in Fault Diagnosis of Turbine Generator Unit[J]. Computer Simulation, 2011, 28(7): 326-332.
[3]牛慧峰, 姜万录. 免疫支持向量机方法在液压泵故障诊断中的应用[J]. 中国机械工程, 2008, 19(14): 1736-1739.
Niu Huifeng, Jiang Wanlu. Application of Hybrid Approach Based on Immune Algorithm and Support Vector Machine for Fault Diagnosis of Hydraulic Pump[J]. China Mechanical Engineering, 2008, 19(14): 1736-1743.
[4]Yang Y, Kamel M. Clustering Ensemble Using Swarm Intelligence[C]//Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, 2003: 65-71.
[5]陈路. 主元分析(PCA)理论分析及应用[EB/OL].[12-05-1]. http://www.cad.zju.edu.cn/home/chenlu/pea.htm.
[6]屈梁生, 张西宁, 沈玉娣著. 机械故障诊断理论与方法[M]. 2009:312.
[7]刘琼荪, 范瑞雅. 确定高斯核参数的聚类方法[J]. 计算机工程与应用, 2011, 47(3): 38-40.
Liu Qiongsun, Fan Ruiya. Method of Determining Gaussian Kernel Parameter by Clustering[J]. Computer Engineering and Applications, 2011, 47(3): 38-40.
[8]Deneubourg J L, Goss S, Franks N, et al. The Dynamics of Collective Sorting: Robot-like Ants and Ant-like Robots[C]//Proceedings of the 1st International Conference on Simulation of Adaptive Behavior: from Animals to Animals. Cambridge,1991:356-363.
[9]Dorigo M, Bonabeaub E, Theraulaz G. Ant Algorithms and Stigmergy[J]. Future Generation Computer Systems, 2000, 16(8): 851-871.
[10]Lumer E, Faieta B. Diversity and Adaptation in Populations of Clustering Ants[C]//Proceedingsof the 3rd International Conference on Simulation of Adaptive Behavior: from Animals to Animals.Cambridge, 1994: 499-508.

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