Fault Classify of Rolling Bearing Based on Time-frequency Generalized Dimension of Vibration Signal and ANFIS

被引:0
|
作者
Fang, Li [1 ]
机构
[1] Dalian Jiaotong Univ, Sch Elect Multiple Units Engn, Dalian 116028, Peoples R China
来源
2017 IEEE 3RD INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC) | 2017年
关键词
multi-fractal; vibration; time frequency matrix; classify;
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
Research shows that multi-fractal can not only exhibit the singular probability distribution form of the fractal signal completely, but also increase the fine level of signal geometrical characteristics and local scaling behavior. Based on multi fractal dimension calculation of time frequency matrix of vibration signal of rolling bearing in this paper, energy distribution characteristics of time-frequency domain of vibration signal could be extracted, then adaptive fuzzy neural network (ANFIS) was used in signal classification. Experiments showed that this method can realize fault classify of rolling bearing effectively, it is feasible in engineering application.
引用
收藏
页码:681 / 684
页数:4
相关论文
共 50 条
  • [1] Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension
    Yuan, Yu
    Zhao, Xing
    Fei, Jiyou
    Zhao, Yulong
    Wang, Jiahui
    SHOCK AND VIBRATION, 2015, 2015
  • [2] The fault diagnosis of the rolling bearing based on the LMD and time-frequency analysis
    Ma, Jun
    Wu, Jiande
    Yuan, Xuyi
    International Journal of Control and Automation, 2013, 6 (04): : 357 - 376
  • [3] Sparse Signal Reconstruction Based on Time-Frequency Manifold for Rolling Element Bearing Fault Signature Enhancement
    He, Qingbo
    Song, Haiyue
    Ding, Xiaoxi
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2016, 65 (02) : 482 - 491
  • [4] Bearing Fault Detection based on Time-frequency Representations of Vibration Signals
    Khang, H. V.
    Karimi, H. R.
    Robbersmyr, K. G.
    2015 18TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS), 2015, : 1970 - 1975
  • [5] Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network
    Sun, Guodong
    Yang, Xiong
    Xiong, Chenyun
    Hu, Ye
    Liu, Moyun
    APPLIED SCIENCES-BASEL, 2022, 12 (10):
  • [6] A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal
    Tong, Qingbin
    Liu, Ziyu
    Lu, Feiyu
    Feng, Ziwei
    Wan, Qingzhu
    SENSORS, 2022, 22 (16)
  • [7] Time-Frequency Fault Feature Extraction for Rolling Bearing Based on the Tensor Manifold Method
    Wang, Fengtao
    Chen, Shouhai
    Sun, Jian
    Yan, Dawen
    Wang, Lei
    Zhang, Lihua
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [8] Time-frequency analysis method of bearing fault diagnosis based on the generalized S transformation
    Cai, Jianhua
    Xiao, Yongliang
    JOURNAL OF VIBROENGINEERING, 2017, 19 (06) : 4221 - 4230
  • [9] Rolling Bearing Fault Diagnosis Based on Time-Frequency Feature Extraction and IBA-SVM
    Zhang, Mei
    Yin, Jun
    Chen, Wanli
    IEEE ACCESS, 2022, 10 : 85641 - 85654
  • [10] A Fault Feature Extraction Method for Rolling Bearing Based on Pulse Adaptive Time-Frequency Transform
    Yao, Jinbao
    Tang, Baoping
    Zhao, Jie
    SHOCK AND VIBRATION, 2016, 2016