不对称样本下基于支持向量机的变压器故障诊断
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摘要
为解决基于支持向量机(SVM)的变压器故障诊断中因样本不对称导致诊断准确率降低的问题,提出了一种改进的向上采样策略和SVM结合的方法。首先通过K-近邻算法提取少数类样本数据中的边界数据集并生成新的少数类随机样本,在此基础上向少数类样本中添加人工生成的随机新样本使得两类样本数量达到基本均衡。对比均衡样本和不对称样本下的SVM分类模型的性能,结果表明:该方法能够有效降低SVM分类平面的偏移程度,进一步提高了SVM变压器故障诊断的准确率。
In the process of power transformer fault diagnosis based on support vector machine(SVM),unbalanceddata makes the classifying hyper plane of SVM shift to the minority samples,which decreases the diagnostic accura-cy. This paper proposed a method combining over sampling strategy and SVM to solve the problem. K-nearest neigh-bor algorithm is used to extract the boundary data sets from the minority samples. On this basis,the new random sam-ples are generated and added into the minority samples to make the two kinds of samples balanced consequently.Comparing the performance of SVM classification model with balanced data sets and unbalanced data sets,the exper-iment results show that the proposed method can reduce the deviation of SVM classifying hyper plane effectively.
In the process of power transformer fault diagnosis based on support vector machine(SVM),unbalanceddata makes the classifying hyper plane of SVM shift to the minority samples,which decreases the diagnostic accura-cy. This paper proposed a method combining over sampling strategy and SVM to solve the problem. K-nearest neigh-bor algorithm is used to extract the boundary data sets from the minority samples. On this basis,the new random sam-ples are generated and added into the minority samples to make the two kinds of samples balanced consequently.Comparing the performance of SVM classification model with balanced data sets and unbalanced data sets,the exper-iment results show that the proposed method can reduce the deviation of SVM classifying hyper plane effectively.
引文
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[11]陶新民,徐晶,童稚靖,等.不均衡数据下基于阴性免疫的过抽样算法[J].控制与决策,2010,25(6):867-873.TAO Xinmin,XU Jing,TONG Zhijing,et al. Over-sampling algorithm based on negative immune in imbalanced data sets learing[J]. Conrol and Decision,2010,25(6):867-873.
[12]陶新民,刘福荣,童智靖,等.不均衡数据下基于SVM的故障检测新算法[J].振动与冲击,2010,29(12):8-12.TAO Xinmin,LIU Furong,TONG Zhijing,et al. Novel fault detection method based on SVM with unbalanced datasets[J]. Journal of Vibration And Shock,2010,29(12):8-12.
[13] HE H,GARCIA E A. Learning from imbalanced data[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(9):1263-1284.
[14] ADDIS A,ARMANO G,VARGIU E. Experimentally studying progressive filtering in presence of input imbalance[M].Berlin:Springer,2013:56-71.
[15]曾志强,吴群,廖备水,等.一种基于核SMOTE的非平衡数据集分类方法[J].电子学报,2009,39(11):2489-2495.ZENG Zhiqiang,WU Qun,LIAO Beishui,et al. A classification method for imbalanced data set based on kernel SMOTE[J].ActaElectronicaSinica,2009,39(11)2489-2495.
[16] CHENK,LUBL,KWOKJ.Efficientclassificationofmultilabel and imbalanced data using min-max modularclassifiers[C]//World Congress on Computation Intelligence.[S.l.]:[s.n.],2006:1770-1775.
[17] MICHAEL D,CAVANAUGH V. Support vector machine for structural abnormality detection[D]. Boston:Northeastern University,2011:30-36.
[18]杨晓伟,郝志峰.支持向量机的算法设计与分析[M].北京:科学出版社,2013:1-114.YANG Xiaowei,HAO Zhifeng. Algorithm design and analysis of support vector machine[M]. Beijing:Science Press,2013:1-114.
[19] LYU G Y,CHEN H Z,ZHANG H B,et al. Fault diagnosis of power transformer based on multi-layer SVM classifier[J]. Electric Power Systems Research,2005,75(1):9-15.
[20]衣柏衡,朱建军,李杰.基于改进SMOTE的小额贷款公司客户信用风险非均衡SVM分类[J].中国管理科学,2016,24(3):24-30.YI Baiheng,ZHU Jianjun,LI Jie. Imbalanced data classification on micro-credit company customer credit risk assessment using improved SMOTE support vector machine[J]. Chinese Journal of Management Science,2016,24(3):24-30.
[21]陶新民,李震,刘福荣,等.基于精简集支持向量机的变压器故障检测方法[J].高电压技术,2016,42(10):3199-3206.TAO Xinmin,LI Zhen,LIU Furong,et al. Fault detection method for power transformer based on SVM using reduced vector set[J]. High Voltage Engineering,2016,42(10):3199-3206.
[22]易辉,宋晓峰,姜斌,等.样本不均衡条件下基于自调整支持向量机的故障诊断[J].北京理工大学学报,2013,33(4):394-398.YI Hui,SONG Xiaofeng,JIANG Bin,et al. Fault diagnosis based on self-turning support vector machine in sample unbalance condition[J]. Transactions of Beijing Institute of Technology,2013,33(4):394-398.
[2] VAPNIC V. The nature of statistical learning theory[M]. New York:Springer,1995:181-205.
[3]郭创新,朱乘治,张琳,等.应用多分类多核学习支持向量机的变压器故障诊断方法[J].中国电机工程学报,2010,30(13):128-134.GUO Chuangxin,ZHU Chengzhi,ZHANG Lin,et al. A fault diagnosis method for power transformer based on multiclass multiple-kernel learning support vector machine[J]. Proceedings of the CSEE,2010,30(13):128-134.
[4]贾立敬,张建文,王传林,等.基于DGA的差分进化支持向量机电力变压器故障诊断[J].高压电器,2015,51(4):13-14.JIA Lijing,ZHANG Jianwen,WANG Chuanlin,et al. Fault diagnosis of power transformer based on DGA of algorithm for SVM[J]. High Voltage Apparatus,2015,51(4):13-14.
[5]陶新民,郝思媛,张冬雪,等.基于样本特性欠取样的不均衡支持向量机[J].控制与决策,2013,28(7):978-984.TAO Xinmin,HAO Siyuan,ZHANG Dongxue,et al. Support vector machine for unbalanced data based on sample properties under-sampling approaches[J]. Conrol and Decision,2013,28(7):978-984.
[6] YUAN J,LI J,ZHANG B. Learning concepts from large scale imbalanced data sets using support cluster machines[C]//ACM Multimedia Conference(MM).[S.l.]:ACM,2006:441-450.
[7]薛薇.非平衡数据集的改进SMOTE再抽样算法[J].统计研究,2012,29(6):95-98.XUE Wei.An improved SMOTE algorithm for re-sampling imbalanced data sets[J]. Statistical Research,2012,29(6):95-98.
[8] SUN Z B,SONG Q B,ZHU X Y,et al. A novel ensemble method for classifying imbalanced data[J]. Pattern Recognition,2015,48(5):1623-1637.
[9]叶志飞,文益民,吕宝粮.不平衡分类问题研究综述[J].智能系统学报,2009,4(2):148-156.YE Zhifei,WEN Yimin,LYU Baoliang. A survey of imbalanced pattern classification problems[J]. Transactions on Intelligent Systems,2009,4(2):148-156.
[10] LUENGO J,FERNáNDEZ A,GARCíA S. Addressing data complexity for imbalanced data sets:Analysis of SMOTEbased oversampling and evolutionary under sampling[J].Soft Computing,2014,15(10):1909-1936.
[11]陶新民,徐晶,童稚靖,等.不均衡数据下基于阴性免疫的过抽样算法[J].控制与决策,2010,25(6):867-873.TAO Xinmin,XU Jing,TONG Zhijing,et al. Over-sampling algorithm based on negative immune in imbalanced data sets learing[J]. Conrol and Decision,2010,25(6):867-873.
[12]陶新民,刘福荣,童智靖,等.不均衡数据下基于SVM的故障检测新算法[J].振动与冲击,2010,29(12):8-12.TAO Xinmin,LIU Furong,TONG Zhijing,et al. Novel fault detection method based on SVM with unbalanced datasets[J]. Journal of Vibration And Shock,2010,29(12):8-12.
[13] HE H,GARCIA E A. Learning from imbalanced data[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(9):1263-1284.
[14] ADDIS A,ARMANO G,VARGIU E. Experimentally studying progressive filtering in presence of input imbalance[M].Berlin:Springer,2013:56-71.
[15]曾志强,吴群,廖备水,等.一种基于核SMOTE的非平衡数据集分类方法[J].电子学报,2009,39(11):2489-2495.ZENG Zhiqiang,WU Qun,LIAO Beishui,et al. A classification method for imbalanced data set based on kernel SMOTE[J].ActaElectronicaSinica,2009,39(11)2489-2495.
[16] CHENK,LUBL,KWOKJ.Efficientclassificationofmultilabel and imbalanced data using min-max modularclassifiers[C]//World Congress on Computation Intelligence.[S.l.]:[s.n.],2006:1770-1775.
[17] MICHAEL D,CAVANAUGH V. Support vector machine for structural abnormality detection[D]. Boston:Northeastern University,2011:30-36.
[18]杨晓伟,郝志峰.支持向量机的算法设计与分析[M].北京:科学出版社,2013:1-114.YANG Xiaowei,HAO Zhifeng. Algorithm design and analysis of support vector machine[M]. Beijing:Science Press,2013:1-114.
[19] LYU G Y,CHEN H Z,ZHANG H B,et al. Fault diagnosis of power transformer based on multi-layer SVM classifier[J]. Electric Power Systems Research,2005,75(1):9-15.
[20]衣柏衡,朱建军,李杰.基于改进SMOTE的小额贷款公司客户信用风险非均衡SVM分类[J].中国管理科学,2016,24(3):24-30.YI Baiheng,ZHU Jianjun,LI Jie. Imbalanced data classification on micro-credit company customer credit risk assessment using improved SMOTE support vector machine[J]. Chinese Journal of Management Science,2016,24(3):24-30.
[21]陶新民,李震,刘福荣,等.基于精简集支持向量机的变压器故障检测方法[J].高电压技术,2016,42(10):3199-3206.TAO Xinmin,LI Zhen,LIU Furong,et al. Fault detection method for power transformer based on SVM using reduced vector set[J]. High Voltage Engineering,2016,42(10):3199-3206.
[22]易辉,宋晓峰,姜斌,等.样本不均衡条件下基于自调整支持向量机的故障诊断[J].北京理工大学学报,2013,33(4):394-398.YI Hui,SONG Xiaofeng,JIANG Bin,et al. Fault diagnosis based on self-turning support vector machine in sample unbalance condition[J]. Transactions of Beijing Institute of Technology,2013,33(4):394-398.
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