Ensemble of One-Class Classifiers for Detecting Faults in Induction Motors

被引:0
|
作者
Zare, Shokoofeh [1 ]
Razavi-Far, Roozbeh [1 ]
Saif, Mehrdad [1 ]
Zarei, Jafar [2 ]
机构
[1] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON, Canada
[2] Shiraz Univ Technol, Sch Elect & Elect Engn, Shiraz, Iran
关键词
RANDOM SUBSPACE METHOD; STRUCTURAL RISK; MINIMIZATION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper studies the use of an ensemble of one-class classifiers for broken rotor bars detection in an induction motors. To achieve this goal, the current signal of induction motor is considered into account for the sake of detection. The fault detector is a multiple classifiers system (MCS), which combines various one-class classifiers to enhance the accuracy of the monitoring system compared to individual one-class classifiers. One-class classifiers are combined in different manners to form the ensembles. These include random subspace, bagging and boosting strategies. These ensemble-based schemes are constructed in homogeneous and heterogeneous configuration and compared together for the purpose of fault detection in induction motors.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] One-Class Classifiers for Detecting Faults In Induction Motors
    Razavi-Far, Roozbeh
    Farajzadeh-Zanjani, Maryam
    Zare, Shokoofeh
    Saif, Mehrdad
    Zarei, Jafar
    2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2017,
  • [2] Dynamic ensemble selection for multi -class classification with one-class classifiers
    Krawczyk, Bartosz
    Galar, Mikel
    Wozniak, Michal
    Bustince, Humberto
    Herrera, Francisco
    PATTERN RECOGNITION, 2018, 83 : 34 - 51
  • [3] One-class classifiers
    Brereton, Richard G.
    JOURNAL OF CHEMOMETRICS, 2011, 25 (05) : 225 - 246
  • [4] Video Anomaly Detection using Ensemble One-class Classifiers
    Li, Gang
    Feng, Zuren
    Lv, Na
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 9343 - 9349
  • [5] Ensemble of One-class Classifiers for Network Intrusion Detection System
    Zainal, Anazida
    Maarof, Mohd Aizaini
    Shamsuddin, Siti Mariyam
    Abraham, Ajith
    FOURTH INTERNATIONAL SYMPOSIUM ON INFORMATION ASSURANCE AND SECURITY, PROCEEDINGS, 2008, : 180 - +
  • [6] Multi-class classification via heterogeneous ensemble of one-class classifiers
    Kang, Seokho
    Cho, Sungzoon
    Rang, Pilsung
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 43 : 35 - 43
  • [7] Intrusion detection in computer networks by a modular ensemble of one-class classifiers
    Giacinto, Giorgio
    Perdisci, Roberto
    Del Rio, Mauro
    Roli, Fabio
    INFORMATION FUSION, 2008, 9 (01) : 69 - 82
  • [8] Ensemble one-class classifiers based on hybrid diversity generation and pruning
    Liu, Jia-Chen
    Miao, Qi-Guang
    Cao, Ying
    Song, Jian-Feng
    Quan, Yi-Ning
    Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2015, 37 (02): : 386 - 393
  • [9] Entropic One-Class Classifiers
    Livi, Lorenzo
    Sadeghian, Alireza
    Pedrycz, Witold
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (12) : 3187 - 3200
  • [10] Detecting Anomalies in the Engine Coolant Sensor using One-Class Classifiers
    da Silva Neto, Eronides E.
    Feitosa, Allan R. S.
    Cavalcanti, George D. C.
    Silva-Filho, Abel G.
    2019 IEEE 90TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-FALL), 2019,