A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

被引:5
|
作者
Felix, Leonardo Oldani [1 ]
Martins, Dionisio Henrique Carvalho de Sa So [1 ]
Monteiro, Ulisses Admar Barbosa Vicente [1 ]
Castro, Brenno Moura [1 ]
Pinto, Luiz Antonio Vaz [1 ]
Martins, Carlos Alfredo Orfao [1 ]
机构
[1] Fed Univ Rio De Janeiro UFRJ, Ctr Tecnol, Ocean Engn Program PENO, Bloco I-108,Cidade Univ, BR-20945970 Rio De Janeiro, RJ, Brazil
关键词
Feature Selection Committee; EMD; PCC; SVM; Random Forest; ANN; FEATURE RANKING; BEARING FAULT; DIAGNOSIS;
D O I
10.1007/s10921-023-00996-0
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Gearboxes are widely used in various industries such as aircrafts, automobiles, wind turbines, ship industries among others. Due its complex configuration, it is a challenging task to identify fault and failures patterns. Its internal components, such as bearings and gears, have different fault patterns, that can appear in one or in both components. The vibration signals were processed using the Empirical Mode Decomposition (EMD) and the Pearson Correlation Coefficient (PCC) to select the significant Intrinsic Mode Functions (IMFs) and then 18 features were extract from this IMFs. Four features ranking techniques [ReliefF, Chi-Square, Max Relevance Min Redundancy (mRMR) and Decision Tree] were used in a committee to select the best feature set, among the 10 with the highest rank, that appears at least in 3 of the 4 methods. The new feature set was used as an input to Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN) algorithms. The results showed that the use of the PCC value as a tool for selecting the significant IMFs, combined with the feature committee led to good results for this classification problem. In this case study, the ANN model outperformed the SVM and the RF algorithms, by using only 4 features to achieve 95.42% of accuracy and 6 features to achieve 100% of accuracy.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] BEARING FAULT DIAGNOSIS USING EMPIRICAL MODE DECOMPOSITION BASED ORDER TRACKING - WIND TURBINE GENERATOR APPLICATION
    Mollasalehi, Ehsan
    Wood, David
    Sun, Qiao
    PROCEEDINGS OF THE 23RD INTERNATIONAL CONGRESS ON SOUND AND VIBRATION: FROM ANCIENT TO MODERN ACOUSTICS, 2016,
  • [32] Gearbox fault diagnosis and prediction based on empirical mode decomposition scheme
    Wang, Jia-Zhong
    Zhou, Gui-Hong
    Zhao, Xiao-Shun
    Liu, Shu-Xia
    PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2007, : 1072 - 1075
  • [33] A Novel Method of Gearbox Health Vibration Monitoring Using Empirical Mode Decomposition
    Dybala, Jacek
    Galezia, Adam
    ADVANCES IN CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS, 2014, : 225 - 234
  • [34] Gearbox Fault Diagnosis Based on Empirical Mode Decomposition and Hilbert Transform
    Liu, Yanli
    Zhang, Dexiang
    Ji, Mingwei
    AUTOMATIC MANUFACTURING SYSTEMS II, PTS 1 AND 2, 2012, 542-543 : 238 - +
  • [35] Identification of tooth fault in a gearbox based on cyclostationarity and empirical mode decomposition
    Kim, Jong-Sik
    Lee, Sang-Kwon
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2018, 17 (03): : 494 - 513
  • [36] AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification
    Asghar, Muhammad Adeel
    Khan, Muhammad Jamil
    Rizwan, Muhammad
    Shorfuzzaman, Mohammad
    Mehmood, Raja Majid
    MULTIMEDIA SYSTEMS, 2022, 28 (04) : 1275 - 1288
  • [37] AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification
    Muhammad Adeel Asghar
    Muhammad Jamil Khan
    Muhammad Rizwan
    Mohammad Shorfuzzaman
    Raja Majid Mehmood
    Multimedia Systems, 2022, 28 : 1275 - 1288
  • [38] FAULT FEATURE ANALYSIS OF THE HIGH SPEED SHAFT BEARING IN WIND TURBINE GEARBOX
    Lin, L.H.
    Lin, Y.H.
    Tsai, J.F.
    Sung, C.C.
    Journal of Taiwan Society of Naval Architects and Marine Engineers, 2019, 38 (01): : 45 - 51
  • [39] Incipient fault diagnosis for wind turbine gearbox based on multidimensional feature evaluation
    Guo F.-H.
    Lin K.
    Dou Y.-F.
    Wu X.
    Yu L.
    Kongzhi yu Juece/Control and Decision, 2024, 39 (05): : 1566 - 1576
  • [40] Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis
    Shi, Ying
    Yi, Cai
    Lin, Jianhui
    Zhuang, Zhe
    Lai, Senhua
    JOURNAL OF VIBRATION AND CONTROL, 2020, 26 (23-24) : 2230 - 2242