Bearing Fault Diagnosis Based on Scale-transformation Stochastic Resonance

被引：1
|
作者：
Cui Ying [1 ]
Zhao Jun [1 ]
Guo Tiantai [1 ]
Song Yuqian [1 ]
机构：
[1] China Jiliang Univ, Coll Metrol & Measurement Engn, Hangzhou 310018, Peoples R China
来源：
SIXTH INTERNATIONAL SYMPOSIUM ON PRECISION MECHANICAL MEASUREMENTS | 2013年 / 8916卷
关键词：
Scale-transformation stochastic resonance (STSR); ensemble empirical mode decomposition (EEMD); weak fault of rolling bearing; slice bi-spectrum;
D O I：
10.1117/12.2035623
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A weak fault feature extraction method of rolling bearing based on scale-transformation stochastic resonance (STSR) is proposed. Combined with ensemble empirical mode decomposition (EEMD), the vibration signal with noise is adaptively decomposed for antialiasing by EEMD method to get intrinsic mode functions (IMFs) of different frequency bands, then the IMFs are inputted into scale-transformation mono-stable system. The low frequency fault features are extracted by using a frequency scale R to change the step length of numerical calculation and the adjustment of mono-stable system parameters, and finally slice bi-spectrum is adopted to perform the postprocessing of the output of the mono-stable system. Simulation analysis is performed to validate the characteristics of STSR, and analysis of measured signal of the rolling bearing with strong background noise shows that the approach can extract the weak fault features of rolling bearing successfully.
引用
收藏
页数：11
相关论文
共 50 条
  • [1] Fault Diagnosis for Rolling Bearing Early Fault Based on Standardization Transformation Stochastic Resonance
    Zhu, Ming
    Jia, Limin
    Wei, Xiukun
    FIFTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2014, : 34 - 40
  • [2] The effect factor of Stochastic Resonance system for Scale-transformation method
    He Xudong
    Yin Mingming
    2015 FIFTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC), 2015, : 1730 - 1733
  • [3] Scale-transformation Stochastic Resonance Application for Processing the Underwater Acoustic Signal
    He Xudong
    2016 IEEE/OES CHINA OCEAN ACOUSTICS SYMPOSIUM (COA), 2016,
  • [4] A novel structure based on stochastic resonance for fault diagnosis of bearing
    Xu, Haitao
    Zhou, Shengxi
    IFAC PAPERSONLINE, 2022, 55 (27): : 399 - 403
  • [5] Application of Stochastic Resonance in Bearing Fault Diagnosis
    Liu, Chaoqin
    Xie, Lei
    Wang, Dong
    Zhou, Guangwu
    Zhou, Qinghua
    Miao, Qiang
    PROCEEDINGS OF 2014 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-2014 HUNAN), 2014, : 223 - 228
  • [6] Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance
    Zhao, Xiaoping
    Wang, Yifei
    Zhang, Yonghong
    Wu, Jiaxin
    Shi, Yunqing
    CMC-COMPUTERS MATERIALS & CONTINUA, 2020, 64 (01): : 571 - 587
  • [7] Weak fault diagnosis of rolling bearing based on improved stochastic resonance
    Zhao X.
    Wang Y.
    Zhang Y.
    Wu J.
    Shi Y.
    Computers, Materials and Continua, 2020, 64 (01): : 571 - 587
  • [8] Bearing fault diagnosis based on variational mode decomposition and stochastic resonance
    Zhang, Xin
    Liu, Huiyu
    Zhang, Heng
    Miao, Qiang
    2018 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), 2018,
  • [9] A PSO Optimization Scale-Transformation Stochastic-Resonance Algorithm With Stability Mutation Operator
    Tong, Ling
    Li, Xiaogang
    Hu, Jinhai
    Ren, Litong
    IEEE ACCESS, 2018, 6 : 1167 - 1176
  • [10] Stochastic Resonance and Self-Induced Stochastic Resonance in Bearing Fault Diagnosis
    Zhang, Shuai
    Yang, Jianhua
    Wang, Canjun
    Liu, Houguang
    Yang, Chen
    FLUCTUATION AND NOISE LETTERS, 2021, 20 (06):
12345