Research on Fault Diagnosis Method Based on Empirical Mode Decomposition & Time-Frequency Reassignment

被引:0
作者
Hao, Zhihua [1 ]
Ma, Zhuang [1 ]
Zhou, Haomiao [1 ]
机构
[1] Tangshan Coll, Dept Informat Engn, Tangshan, Peoples R China
来源
MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷
关键词
Reassignment method; Empirical Mode Decomposition; Fault Diagnosis; Cross-term;
D O I
10.4028/www.scientific.net/AMR.433-440.6256
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The reassignment method is a technique for sharpening a time-frequency representation by mapping the data to time-frequency coordinates that are nearer to the true region of support of the analyzed signal. The reassignment method has been proved to produce a better localization of the signal components and improve the readability of the time-frequency representation by concentrating its energy at a center of gravity. But there are still few cross-terms. Then, the empirical mode decomposition is introduced to the reassignment method to suppress the interference of the cross-term encountered in processing the multi-component signals. The multi-component signal can be decomposed into a finite number intrinsic mode function by using EMD. Then, the reassignment method can be calculated for each of the intrinsic mode function. Simulation analysis is presented to show that this method can improve the localization of time-frequency representation and reduce the cross terms. The vibration signals measured from diesel engine in the stage of deflagrate were analyzed with the reassignment method. Experimental results indicate that this method has good potential in mechanical fault feature extraction.
引用
收藏
页码:6256 / 6261
页数:6
相关论文
共 50 条
  • [31] A review on empirical mode decomposition in fault diagnosis of rotating machinery
    Lei, Yaguo
    Lin, Jing
    He, Zhengjia
    Zuo, Ming J.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2013, 35 (1-2) : 108 - 126
  • [32] Applications of improved empirical mode decomposition in machinery fault diagnosis
    Ma, Wenpeng
    Zhang, Junhong
    Ma, Liang
    Liu, Yu
    Jia, Xiaojie
    [J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2015, 35 (04): : 637 - 644
  • [33] A Fault Diagnosis Method for Drilling Pump Fluid Ends Based on Time-Frequency Transforms
    Tang, Aimin
    Zhao, Wu
    [J]. PROCESSES, 2023, 11 (07)
  • [34] A bearing fault extraction method combining time-frequency mode decomposition based on local maxima with amplitude z-scores
    Liu, Tao
    Li, Xinsan
    Lyu, Mindong
    Yan, Shaoze
    [J]. ADVANCED ENGINEERING INFORMATICS, 2025, 64
  • [35] ICE Fault Diagnosis Method Based on Mutual Information and WVD Time-Frequency Analysis
    Wang, Xinjun
    Cai, Yanping
    Lin, Xuze
    [J]. Development of Industrial Manufacturing, 2014, 525 : 741 - 745
  • [36] An Automatic Fault Diagnosis Method for Aerospace Rolling Bearings Based on Ensemble Empirical Mode Decomposition
    Wang, Hong
    Liu, Hongxing
    Qing, Tao
    Liu, Wenyang
    He, Tian
    [J]. 2017 8TH INTERNATIONAL CONFERENCE ON MECHANICAL AND AEROSPACE ENGINEERING (ICMAE), 2017, : 502 - 506
  • [37] A Fault Diagnosis Method for Automaton Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
    Wang, F.
    Fang, L.
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING, 2019, 32 (06): : 877 - 883
  • [38] A Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
    Wang, F.
    Fang, L.
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING, 2019, 32 (07): : 1010 - 1016
  • [39] A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation
    Song, Yueheng
    Zeng, Shengkui
    Ma, Jiming
    Guo, Jianbin
    [J]. MEASUREMENT, 2018, 117 : 266 - 276
  • [40] Multimodal time-frequency graph fusion based fault diagnosis
    Yang, Hongyan
    Yao, Qi
    [J]. 2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024, 2024,