An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction

被引:2
|
作者
He, Fengxia [1 ]
Zheng, Chuansheng [1 ]
Pang, Chao [2 ]
Zhao, Chengying [1 ]
Yang, Mingyang [2 ]
Zhu, Yunpeng [3 ]
Luo, Zhong [2 ]
Luo, Haitao [4 ]
Li, Lei [2 ]
Jiang, Haotian [5 ]
机构
[1] Shenyang Jianzhu Univ, Sch Mech Engn, Shenyang 110168, Peoples R China
[2] Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Peoples R China
[3] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England
[4] Chinese Acad Sci, Shenyang Inst Automat, Key Lab Robot, Shenyang 110016, Peoples R China
[5] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China
关键词
bearing faults; fault separation; IES-CYCBD; complex faults; fault diagnosis; FAST COMPUTATION; DIAGNOSTICS; VIBRATION; DEMODULATION; ALGORITHM; BAND;
D O I
10.3390/s24030951
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum-maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.
引用
收藏
页数:22
相关论文
共 50 条
  • [31] Feature extraction method based on adaptive and concise empirical wavelet transform and its applications in bearing fault diagnosis
    Zhang, Kun
    Ma, Chaoyong
    Xu, Yonggang
    Chen, Peng
    Du, Jianxi
    MEASUREMENT, 2021, 172
  • [32] Weighted envelope spectrum based on reselection mechanism and its application in bearing fault diagnosis
    Zhang, Yongxiang
    Huang, Baoyu
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (04)
  • [33] Composite fault feature extraction of rolling bearing using adaptive circulant singular spectrum analysis
    Zhou, Hongdi
    Zhu, Lin
    Zhong, Fei
    Cai, Yijie
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (12)
  • [34] Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion
    Zhu, Huibin
    He, Zhangming
    Wei, Juhui
    Wang, Jiongqi
    Zhou, Haiyin
    SENSORS, 2021, 21 (07)
  • [35] Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method
    Min Li
    Jianhong Yang
    Xiaojing Wang
    Chinese Journal of Mechanical Engineering, 2015, 28 : 1240 - 1247
  • [36] Fault Feature Extraction of Rolling Bearing Based on an Improved Cyclical Spectrum Density Method
    LI Min
    YANG Jianhong
    WANG Xiaojing
    Chinese Journal of Mechanical Engineering, 2015, 28 (06) : 1240 - 1247
  • [37] Fault Feature Extraction of Rolling Bearing Based on an Improved Cyclical Spectrum Density Method
    Li Min
    Yang Jianhong
    Wang Xiaojing
    CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2015, 28 (06) : 1240 - 1247
  • [38] Robust rolling bearing fault feature extraction method based on cyclic spectrum analysis
    Yan, Yunhai
    Guo, Yu
    Wu, Xing
    Zhendong yu Chongji/Journal of Vibration and Shock, 2022, 41 (06): : 1 - 7
  • [39] Fault Feature Extraction of Rolling Bearing Based on an Improved Cyclical Spectrum Density Method
    LI Min
    YANG Jianhong
    WANG Xiaojing
    Chinese Journal of Mechanical Engineering, 2015, (06) : 1240 - 1247
  • [40] Multilevel Feature Extraction Method for Adaptive Fault Diagnosis of Railway Axle Box Bearing
    Liu, Zhigang
    Zhang, Long
    Xiong, Guoliang
    SHOCK AND VIBRATION, 2023, 2023