Fault Feature Extraction of Gearboxes Using Ensemble Empirical Mode Decomposition

被引:0
|
作者
Lin, Jinshan [1 ]
机构
[1] Weifang Univ, Sch Mech & Elect Engn, Weifang, Shandong, Peoples R China
关键词
feature extraction; gearbox; emsemble empirical mode decomposition(EEMD); intrinsic mode function(IMF);
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The paper employs ensemble empirical mode decomposition (EEMD) to extract the fault information from the signal collected from a defective gearbox. In view of the shortcoming of the mode mixing which empirical mode decomposition (EMD) fails to overcome, the EEMD method is used to decompose the signal captured from the defective gearbox and successfully separate the different components from high frequency to low frequency. Then, the first four intrinsic mode functions (IMFs), containing the most energy of the signal, are extracted; by analyzing the spectrum of the first four components, we succeed in uncovering the reason causing the fault of the gearbox. The results show that the EEMD method could be feasible to diagnose the fault of the gearbox.
引用
收藏
页码:478 / 483
页数:6
相关论文
共 50 条
  • [41] Intelligent fault diagnosis in power plant using empirical mode decomposition, fuzzy feature extraction and support vector machines
    Hu, Q
    He, ZJ
    Zi, YY
    Zhang, ZS
    Lei, YG
    DAMAGE ASSESSMENT OF STRUCTURES VI, 2005, 293-294 : 373 - 381
  • [42] Entropy-based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise
    Nalband, Saif
    Prince, Amalin
    Agrawal, Anita
    IET SCIENCE MEASUREMENT & TECHNOLOGY, 2018, 12 (03) : 350 - 359
  • [43] Ensemble empirical mode decomposition based feature enhancement of cardio signals
    Janusauskas, Arturas
    Marozas, Vaidotas
    Lukosevicius, Arunas
    MEDICAL ENGINEERING & PHYSICS, 2013, 35 (08) : 1059 - 1069
  • [44] Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis
    Atik Faysal
    Wai Keng Ngui
    M. H. Lim
    Journal of Vibration Engineering & Technologies, 2021, 9 : 2229 - 2245
  • [45] Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis
    Faysal, Atik
    Ngui, Wai Keng
    Lim, M. H.
    JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES, 2021, 9 (08) : 2229 - 2245
  • [46] Application of Ensemble Empirical Mode Decomposition to Diagnosis Bladed Disk Fault
    Bouhali, Rima
    Tadjine, Kamel
    Saadi, Mohamed Nacer
    Bandjama, Hocine
    PROCEEDINGS OF 2016 8TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION & CONTROL (ICMIC 2016), 2016, : 332 - 337
  • [47] Enhancing the Ability of Ensemble Empirical Mode Decomposition in Machine Fault Diagnosis
    Guo, Wei
    Tse, Peter W.
    2010 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE, 2010, : 301 - 307
  • [48] Image Feature Extraction and Analysis Based on Empirical Mode Decomposition
    Huang, Shiqi
    Zhang, Yucheng
    Liu, Zhe
    PROCEEDINGS OF 2016 IEEE ADVANCED INFORMATION MANAGEMENT, COMMUNICATES, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IMCEC 2016), 2016, : 615 - 619
  • [49] A deep feature extraction method for bearing fault diagnosis based on empirical mode decomposition and kernel function
    Wang, Fengtao
    Deng, Gang
    Liu, Chenxi
    Su, Wensheng
    Han, Qingkai
    Li, Hongkun
    ADVANCES IN MECHANICAL ENGINEERING, 2018, 10 (09)
  • [50] A fault detection strategy using the enhancement ensemble empirical mode decomposition and random decrement technique
    Xiang, Jiawei
    Zhong, Yongteng
    MICROELECTRONICS RELIABILITY, 2017, 75 : 317 - 326