一种基于分子结构设计理论的聚类分析方法
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摘要
针对常用的无监督聚类分析方法中存在的问题,提出了一种基于分子结构设计理论的聚类分析方法。该方法借鉴分子结构设计理论模型,将故障样本空间看作分子系统,将故障样本看作分子系统中的原子,以故障样本之间的差异度作为分子势能的度量指标,在故障样本间"相互作用势"的影响下,以样本间"势能"最小为依据,调整故障样本在映射平面上的位置,从而获得最佳的聚类效果。开展了不同状态滚动轴承振动测试实验,聚类结果表明,相比于SOM聚类方法,该方法将聚类有效性指标DB值降低49.04%。将该方法应用于柴油机故障振动数据的聚类中,实验结果表明聚类效果良好,能够有效地将不同故障的数据区分开,验证了该方法的可行性和有效性。
Aiming at defects of the commonly used unsupervised clustering analysis method,a new clustering analysis method based on the theory of molecular structure design was proposed. The proposed method drew a lesson from a theoretical model of molecular structure design,and it took a fault sample space as a molecular system,fault samples as atoms in the molecule system,diversities among fault samples as the measurement index of molecular potential energy.Under the influence of the interaction potential among fault samples,taking that the potential energy among samples was the minimum as a criterion,positions of fault samples on a mapping plane were adjusted to get the optimal clustering results. Vibration tests for rolling bearings under different conditions were conducted. The clustering results showed that compared with the SOM clustering method,the proposed method reduces the clustering effectiveness index d B value by49. 04%. The proposed method was also applied in clustering diesel engines' fault vibration data. The test results showed that its clustering effect is good,it can effectively separate different faults' data regions; the feasibility and effectiveness of the proposed method are verified.
Aiming at defects of the commonly used unsupervised clustering analysis method,a new clustering analysis method based on the theory of molecular structure design was proposed. The proposed method drew a lesson from a theoretical model of molecular structure design,and it took a fault sample space as a molecular system,fault samples as atoms in the molecule system,diversities among fault samples as the measurement index of molecular potential energy.Under the influence of the interaction potential among fault samples,taking that the potential energy among samples was the minimum as a criterion,positions of fault samples on a mapping plane were adjusted to get the optimal clustering results. Vibration tests for rolling bearings under different conditions were conducted. The clustering results showed that compared with the SOM clustering method,the proposed method reduces the clustering effectiveness index d B value by49. 04%. The proposed method was also applied in clustering diesel engines' fault vibration data. The test results showed that its clustering effect is good,it can effectively separate different faults' data regions; the feasibility and effectiveness of the proposed method are verified.
引文
[1]吴俊芬,胡念苏,赵瑜.基于自组织特征映射神经网络的汽轮机回热系统故障诊断[J].武汉大学学报(工学版),2003,36(4):99-102.WU Junfen,HU Niansu,ZHAO Yu.Fault diagnosis of steam turbine regenerative system based on self-organizing neural network[J].Journal of Wuhan University of Hydraulic and Electric Engineering,2003,36(4):99-102.
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[9]姚拓中,安鹏,宋加涛.基于历史分类加权和分级竞争采样的多视角主动学习[J].电子学报,2017,45(1):46-53.YAO Tuozhong,AN Peng,SONG Jiatao.Multi-view active learning based on weighted hypothesis boosting and hierarchical competition sampling[J].Acta Electronica Sinica,2017,45(1):46-53.
[10]陈佳,石林.数据挖掘中模糊C聚类算法的寻优能力优化[J].科技通报,2015,31(9):208-211.CHEN Jia,SHI Lin.Optimization of the fuzzy C clustering algorithm in data mining[J].Bulletin of Science and Technology,2015,31(9):208-211.
[11]徐增丙,李友荣,王志刚,等.基于ART和Yu范数的聚类方法在齿轮故障诊断中的应用[J].武汉科技大学学报,2016,39(2):116-120.XU Zengbing,LI Yourong,WANG Zhigang.Application of clustering method based on ART and Yu norm to gears fault diagnosis[J].Journal of Wuhan University of Science and Technology,2016,39(2):116-120.
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[14]张淑清,胡永涛,李盼,等.基于MEMD互近似熵及FCM聚类的轴承故障诊断方法[J].中国机械工程,2015,26(19):2613-2618.ZHANG Shuqing,HU Yongtao,LI Pan,et al.Fault diagnosis method for bearings based on MEMD cross approximate entropy and FCM clustering[J].China Mechanical Engineering,2015,26(19):2613-2618.
[15]MAZZEO G M,MASCIARI E,ZANIOLO C.A fast and accurate algorithm for unsupervised clustering around centroids[J].Information Sciences,2017,400/401:63-90.
[16]余琳,邓玲.常见聚类算法的比较以及DSS系统中的应用[C]∥湖北省通信学会、武汉通信学会2009年学术年会论文集.武汉:湖北省通信学会、武汉通信学会,2009.
[17]束洪春,田鑫萃.基于PCA聚类方法的±800 k V直流输电线路全线速动保护[J].电力自动化设备,2016,36(1):51-59.SHU Hongchun,TIAN Xincui.Entire-line instant protection based on PCA clustering for±800 k V HVDC transmission line[J].Electric Power Automation Equipment,2016,36(1):51-59.
[18]王继丽.聚类融合算法研究及其在电信中的应用[D].浙江:浙江大学,2008.
[19]雷璐宁.一种改进SOM神经网络及其在水质评价中的应用研究[D].重庆:重庆大学,2009.
[20]杨松,于凤芹.利用遗传算法改进SOM网络初始权值的乐器分类[J].计算机系统应用,2012,21(4):238-240.YANG Song,YU Fengqin.Using genetic algorithms to improve the initial weights of SOM network in the musical instrument classification[J].Computer Systems&Applications,2012,21(4):238-240.
[21]邹瑜,帅仁俊.基于改进的SOM神经网络的医学图像分割算法[J].计算机工程与设计,2016,37(9):2533-2537.ZOU Yu,SHUAI Renjun.Improved segmentation algorithm of medical images based on SOM neural network[J].Computer Engineering and Design,2016,37(9):2533-2537.
[22]陶刚,闫永刚,刘俊,等.基于改进的SOM聚类连续属性离散化算法[J].计算机应用,2015,35(增刊1):89-92.TAO Gang,YAN Yonggang,LIU Jun,et al.Discrimination of continuous attributes based on improved SOM clustering[J].Journal of Computer Applications,2015,35(Sup1):89-92.
[23]李斌,狄岚,王少华,等.基于改进核模糊C均值类间极大化聚类算法[J].计算机应用,2016,36(7):1981-1987.LI Bin,DI Lan,WANG Shaohua,et al.Clustering algorithm with maximum distance between clusters based on improved kernel fuzzy C-means[J].Journal of Computer Applications,2016,36(7):1981-1987.
[24]吉青,杨小震.分子力场发展的新趋势[J].化学通报,2005,68(2):111-116.JI Qing,YANG Xiaozhen.New trend on molecular force field[J].Chemistry,2005,68(2):111-116.
[25]程涛.用分子力学力场方法计算热力学性质以及预测极端状态下的状态函数[D].上海:上海交通大学,2010.
[26]周世兵,徐振源,唐旭清.K-means算法最佳聚类数确定方法[J].计算机应用,2010,30(8):1995-1998.ZHOU Shibing,XU Zhenyuan,TANG Xuqing.Method for determining optimal number of clusters in K-means clustering algorithm[J].Journal of Computer Applications,2010,30(8):1995-1998.
[27]ZHANG Xining,GUO Jinliang,LI Bing.Chromatometry based diagnosis method and its application in condition recognition of diesel engine[C]∥Proceedings of the 2012Third International Conference on Mechanic Automation and Control Engineering.Baotou:IEEE Computer Society,2012.
[28]彭丽.数据挖掘中几种划分聚类算法的比较及改进[D].大连:大连理工大学,2008.
[2]雷亚国,何正嘉,訾艳阳.基于混合智能新模型的故障诊断[J].机械工程学报,2008,44(7):112-117.LEI Yaguo,HE Zhengjia,ZI Yanyang.Fault diagnosis based on novel hybrid intelligent model[J].Chinese Journal of Mechanical Engineering,2008,44(7):112-117.
[3]BARALDI P,DI MAIO F,RIGAMONTI M,et al.Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients[J].Mechanical Systems and Signal Processing,2015,58/59:160-178.
[4]李状.基于无监督学习的风电机组传动链智能故障诊断方法研究[D].北京:华北电力大学,2016.
[5]林辉.基于粒子群和模糊数学的入侵检测系统的研究[J].电子设计工程,2016,24(14):24-26.LIN Hui.Research on intrusion detection system based on particle swarm optimization and fuzzy mathematics[J].Electronic Design Engineering,2016,24(14):24-26.
[6]刘帅.面向网络数据流的多层面异常行为分析检测技术研究[D].郑州:解放军信息工程大学,2015.
[7]严英杰,盛戈皞,陈玉峰,等.基于大数据分析的输变电设备状态数据异常检测方法[J].中国电机工程学报,2015,35(1):52-59.YAN Yingjie,SHENG Gehao,CHEN Yufeng,et al.An method for anomaly detection of state information of power equipment based on big data analysis[J].Proceedings of the CSEE,2015,35(1):52-59.
[8]崔晓宇.基于改进聚类算法医学图像的分割与应用[D].大连:大连理工大学,2015.
[9]姚拓中,安鹏,宋加涛.基于历史分类加权和分级竞争采样的多视角主动学习[J].电子学报,2017,45(1):46-53.YAO Tuozhong,AN Peng,SONG Jiatao.Multi-view active learning based on weighted hypothesis boosting and hierarchical competition sampling[J].Acta Electronica Sinica,2017,45(1):46-53.
[10]陈佳,石林.数据挖掘中模糊C聚类算法的寻优能力优化[J].科技通报,2015,31(9):208-211.CHEN Jia,SHI Lin.Optimization of the fuzzy C clustering algorithm in data mining[J].Bulletin of Science and Technology,2015,31(9):208-211.
[11]徐增丙,李友荣,王志刚,等.基于ART和Yu范数的聚类方法在齿轮故障诊断中的应用[J].武汉科技大学学报,2016,39(2):116-120.XU Zengbing,LI Yourong,WANG Zhigang.Application of clustering method based on ART and Yu norm to gears fault diagnosis[J].Journal of Wuhan University of Science and Technology,2016,39(2):116-120.
[12]张淑清,包红燕,李盼,等.基于RQA与GG聚类的滚动轴承故障识别[J].中国机械工程,2015,26(10):1385-1390.ZHANG Shuqing,BAO Hongyan,LI Pan,et al.Fault diagnosis of rolling bearing based on RQA and GG clustering[J].China Mechanical Engineering,2015,26(10):1385-1390.
[13]王军辉,贾嵘,谭泊.基于EEMD和模糊C均值聚类的风电机组齿轮箱故障诊断[J].太阳能学报,2015,36(2):319-324.WANG Junhui,JIA Rong,TAN Bo.Fault diagnosis of wind turbine’s gearbox based on eemd and fuzzy C means glustering[J].Acta Energiae Solaris Sinica,2015,36(2):319-324.
[14]张淑清,胡永涛,李盼,等.基于MEMD互近似熵及FCM聚类的轴承故障诊断方法[J].中国机械工程,2015,26(19):2613-2618.ZHANG Shuqing,HU Yongtao,LI Pan,et al.Fault diagnosis method for bearings based on MEMD cross approximate entropy and FCM clustering[J].China Mechanical Engineering,2015,26(19):2613-2618.
[15]MAZZEO G M,MASCIARI E,ZANIOLO C.A fast and accurate algorithm for unsupervised clustering around centroids[J].Information Sciences,2017,400/401:63-90.
[16]余琳,邓玲.常见聚类算法的比较以及DSS系统中的应用[C]∥湖北省通信学会、武汉通信学会2009年学术年会论文集.武汉:湖北省通信学会、武汉通信学会,2009.
[17]束洪春,田鑫萃.基于PCA聚类方法的±800 k V直流输电线路全线速动保护[J].电力自动化设备,2016,36(1):51-59.SHU Hongchun,TIAN Xincui.Entire-line instant protection based on PCA clustering for±800 k V HVDC transmission line[J].Electric Power Automation Equipment,2016,36(1):51-59.
[18]王继丽.聚类融合算法研究及其在电信中的应用[D].浙江:浙江大学,2008.
[19]雷璐宁.一种改进SOM神经网络及其在水质评价中的应用研究[D].重庆:重庆大学,2009.
[20]杨松,于凤芹.利用遗传算法改进SOM网络初始权值的乐器分类[J].计算机系统应用,2012,21(4):238-240.YANG Song,YU Fengqin.Using genetic algorithms to improve the initial weights of SOM network in the musical instrument classification[J].Computer Systems&Applications,2012,21(4):238-240.
[21]邹瑜,帅仁俊.基于改进的SOM神经网络的医学图像分割算法[J].计算机工程与设计,2016,37(9):2533-2537.ZOU Yu,SHUAI Renjun.Improved segmentation algorithm of medical images based on SOM neural network[J].Computer Engineering and Design,2016,37(9):2533-2537.
[22]陶刚,闫永刚,刘俊,等.基于改进的SOM聚类连续属性离散化算法[J].计算机应用,2015,35(增刊1):89-92.TAO Gang,YAN Yonggang,LIU Jun,et al.Discrimination of continuous attributes based on improved SOM clustering[J].Journal of Computer Applications,2015,35(Sup1):89-92.
[23]李斌,狄岚,王少华,等.基于改进核模糊C均值类间极大化聚类算法[J].计算机应用,2016,36(7):1981-1987.LI Bin,DI Lan,WANG Shaohua,et al.Clustering algorithm with maximum distance between clusters based on improved kernel fuzzy C-means[J].Journal of Computer Applications,2016,36(7):1981-1987.
[24]吉青,杨小震.分子力场发展的新趋势[J].化学通报,2005,68(2):111-116.JI Qing,YANG Xiaozhen.New trend on molecular force field[J].Chemistry,2005,68(2):111-116.
[25]程涛.用分子力学力场方法计算热力学性质以及预测极端状态下的状态函数[D].上海:上海交通大学,2010.
[26]周世兵,徐振源,唐旭清.K-means算法最佳聚类数确定方法[J].计算机应用,2010,30(8):1995-1998.ZHOU Shibing,XU Zhenyuan,TANG Xuqing.Method for determining optimal number of clusters in K-means clustering algorithm[J].Journal of Computer Applications,2010,30(8):1995-1998.
[27]ZHANG Xining,GUO Jinliang,LI Bing.Chromatometry based diagnosis method and its application in condition recognition of diesel engine[C]∥Proceedings of the 2012Third International Conference on Mechanic Automation and Control Engineering.Baotou:IEEE Computer Society,2012.
[28]彭丽.数据挖掘中几种划分聚类算法的比较及改进[D].大连:大连理工大学,2008.