• <label id="vq7pgu"><acronym id="vq7pgu"></acronym><address id="vq7pgu"></address><q id="vq7pgu"></q><kbd id="vq7pgu"></kbd></label><thead id="vq7pgu"><bdo id="vq7pgu"></bdo><strike id="vq7pgu"></strike></thead><form id="vq7pgu"><th id="vq7pgu"></th></form><dir id="vq7pgu"><button id="vq7pgu"></button><b id="vq7pgu"></b><code id="vq7pgu"></code></dir><i id="vq7pgu"><dt id="vq7pgu"><small id="vq7pgu"></small><dt id="vq7pgu"></dt></dt><legend id="vq7pgu"><address id="vq7pgu"></address><ul id="vq7pgu"></ul></legend></i><fieldset id="vq7pgu"><th id="vq7pgu"><center id="vq7pgu"></center><dfn id="vq7pgu"></dfn><li id="vq7pgu"></li><form id="vq7pgu"></form></th><table id="vq7pgu"><dir id="vq7pgu"></dir><kbd id="vq7pgu"></kbd><i id="vq7pgu"></i><q id="vq7pgu"></q></table></fieldset><b id="vq7pgu"><i id="vq7pgu"><option id="vq7pgu"></option><bdo id="vq7pgu"></bdo><noframes id="vq7pgu">
  • <kbd id="vq7pgu"></kbd>
  • <kbd id="vq7pgu"></kbd><b id="vq7pgu"></b><font id="vq7pgu"></font><ol id="vq7pgu"></ol>

                  [1]劉晨斐,崔昊楊,李 鑫,等.不對稱樣本下基于支持向量機的變壓器故障診斷[J].高壓電器,2019,55(07):216-220.[doi:10.13296/j.1001-1609.hva.2019.07.031]
                   LIU Chenfei,CUI Haoyang,LI Xin,et al.Transformers Fault Diagnosis Based on SVM for Unbalanced Data[J].High Voltage Apparatus,2019,55(07):216-220.[doi:10.13296/j.1001-1609.hva.2019.07.031]
                  點擊複制

                  不對稱樣本下基于支持向量機的變壓器故障診斷()
                  分享到:

                  《高壓電器》[ISSN:1001-1609/CN:61-11271/TM]

                  卷:
                  第55卷
                  期數:
                  2019年07期
                  頁碼:
                  216-220
                  欄目:
                  技術討論
                  出版日期:
                  2019-07-31

                  文章信息/Info

                  Title:
                  Transformers Fault Diagnosis Based on SVM for Unbalanced Data
                  作者:
                  劉晨斐 崔昊楊 李 鑫 束 江 李 亞
                  (上海電力學院電子與信息工程學院, 上海 200090)
                  Author(s):
                  LIU Chenfei CUI Haoyang LI Xin SHU Jiang LI Ya
                  (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China)
                  關鍵詞:
                  故障診斷 支持向量機 不對稱樣本 上采樣
                  Keywords:
                  fault diagnosis SVM unbalanced data over sampling
                  DOI:
                  10.13296/j.1001-1609.hva.2019.07.031
                  摘要:
                  爲解決基于支持向量機(SVM)的變壓器故障診斷中因樣本不對稱導致診斷准確率降低的問題,提出了一種改進的向上采樣策略和SVM結合的方法。首先通過K-近鄰算法提取少數類樣本數據中的邊界數據集並生成新的少數類隨機樣本,在此基礎上向少數類樣本中添加人工生成的隨機新樣本使得兩類樣本數量達到基本均衡。對比均衡樣本和不對稱樣本下的SVM分類模型的性能,結果表明:該方法能夠有效降低SVM分類平面的偏移程度,進一步提高了SVM變壓器故障診斷的准確率。
                  Abstract:
                  In the process of power transformer fault diagnosis based on support vector machine (SVM), unbalanced data makes the classifying hyper plane of SVM shift to the minority samples, which decreases the diagnostic accuracy. This paper proposed a method combining over sampling strategy and SVM to solve the problem. K- nearest neighbor algorithm is used to extract the boundary data sets from the minority samples. On this basis, the new random samples 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 experiment results show that the proposed method can reduce the deviation of SVM classifying hyper plane effectively.

                  參考文獻/References:

                  [1] KANO M, NAKAGAWA Y. Data-based process monitoring, process controland quality improvement:Recent developments and applications in steel industry[J]. Computers & Chemical Engineering,2008,32(1-2):12-24.
                  [2] VAPNIC V. The nature of statistical learning theory[M]. New York:Springer,1995:181-205.
                  [3] 郭創新,朱乘治,張 琳,等. 應用多分類多核學習支持向量機的變壓器故障診斷方法[J]. 中國電機工程學報,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] 賈立敬,張建文,王傳林,等.基于DGA的差分進化支持向量機電力變壓器故障診斷[J]. 高壓電器,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,張冬雪,等. 基于樣本特性欠取樣的不均衡支持向量機[J]. 控制與決策,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. 非平衡數據集的改進SMOTE再抽樣算法[J]. 統計研究,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] 葉志飛,文益民,呂寶糧. 不平衡分類問題研究綜述[J]. 智能系統學報,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 SMOTE-based oversampling and evolutionary under sampling[J]. Soft Computing,2014,15(10):1909-1936.
                  [11] 陶新民,徐 晶,童稚靖,等. 不均衡數據下基于陰性免疫的過抽樣算法[J]. 控制與決策,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,劉福榮,童智靖,等. 不均衡數據下基于SVM的故障檢測新算法[J]. 振動與沖擊,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] 曾志強,吳 群,廖備水,等. 一種基于核SMOTE 的非平衡數據集分類方法[J]. 電子學報,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]. Acta Electronica Sinica,2009,39(11)2489-2495.
                  [16] CHEN K, LU B L, KWOK J. Efficient classification of multi-label 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] 楊曉偉,郝志峰. 支持向量機的算法設計與分析[M]. 北京: 科學出版社,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,朱建軍,李 傑. 基于改進SMOTE的小額貸款公司客戶信用風險非均衡SVM分類[J]. 中國管理科學,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,劉福榮,等. 基于精簡集支持向量機的變壓器故障檢測方法[J]. 高電壓技術,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 輝,宋曉峰,姜 斌,等.樣本不均衡條件下基于自調整支持向量機的故障診斷[J]. 北京理工大學學報,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.c

                  備注/Memo

                  備注/Memo:
                  劉晨斐(1991—),男,碩士研究生,研究領域爲電力設備狀態監測與故障診斷。 崔昊楊(1978—),男,教授,博士,從事電力設備狀態檢測研究(通訊作者)。 收稿日期:2018-11-21; 修回日期:2019-01-25 基金項目:國家自然科學基金資助項目(61107081,61401269);上海市地方能力建設項目資助課題(15110500900, 14110500900)。 Project Supported by National Natural Science Foundation of China(61107081,61401269),Shanghai Local Colleges and Universities Capacity Building Program(15110500900,14110500900).
                  更新日期/Last Update: 2019-07-15