Improved EMD with a Soft Sifting Stopping Criterion and Its Application to Fault Diagnosis of Rotating Machinery

被引:10
|
作者
Peng D. [1 ]
Liu Z. [1 ,2 ]
Jin Y. [1 ]
Qin Y. [2 ]
机构
[1] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu
[2] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing
关键词
EMD; Fault diagnosis; High-speed train; Mode mixing; Sifting stopping criterion;
D O I
10.3901/JME.2019.10.122
中图分类号
学科分类号
摘要
The sifting stopping criterion of empirical mode decomposition is one of the key factors affecting the accuracy of fault diagnosis of rotating machinery in high-speed trains. The current method of determining thresholds in advance is generally adopted, which is not adaptive. Such methods lead to the problem of mode mixing in empirical mode decomposition, which affects the fault diagnosis results. The influence of sifting stopping criterion on the empirical mode decomposition results are fully demonstrated, and then a soft sifting stopping criterion that can adaptively monitor the sifting process is proposed. This criterion is used to suppress the mode mixing problem and improve the accuracy and efficiency of empirical mode decomposition. Aiming at the target signal, the criterion defines an objective function that describes the global energy and local impact characteristics, and combines a heuristic mechanism to realize the optimization of the sifting iterations number in each sifting process, so as to guarantee the empirical mode decomposition to obtain the optimal decomposition results. Based on the simulation data and the Case Western Reserve University Bearing Data, the improved empirical mode decomposition is compared with the two traditional methods in different decomposition and diagnostic performance dimensions. Finally, the proposed improved EMD is successfully applied to the fault diagnosis case of rotating machine simulation test rig of high-speed trains. © 2019 Journal of Mechanical Engineering.
引用
收藏
页码:122 / 132
页数:10
相关论文
共 27 条
  • [1] Liu Z., Pan D., Zuo M., Et al., A review on fault diagnosis for rail vehicles, Journal of Mechanical Engineering, 52, 14, pp. 134-146, (2016)
  • [2] Staszewski W.J., Worden K., Tomlinso G.R., Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition, Mechanical Systems and Signal Processing, 11, 5, pp. 673-692, (1997)
  • [3] Gabor D., Theory of communication, Journal of the Institution of Electrical Engineers, 94, 73, (2011)
  • [4] Lin J., Zuo M.J., Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing, 17, 6, pp. 1259-1269, (2003)
  • [5] Huang N.E., Shen Z., Long S.R., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings Mathematical Physical and Engineering Sciences, 454, 1971, pp. 903-995, (1998)
  • [6] Zhao J., Yang Y., Li T., Et al., Application of Empirical Mode Decomposition and Fuzzy Entropy to High-speed Rail Fault Diagnosis, (2014)
  • [7] He X., Chang Y., A novel approach for reliable gearbox fault diagnosis in high-speed train driving system based on nonlinear feature extraction, Modern Manufacturing Engineering, 6, pp. 31-39, (2015)
  • [8] Qin N., Jin W., Huang J., Et al., Feature extraction of high speed train bogie based on ensemble empirical mode decomposition and sample entropy, Journal of Southwest Jiaotong University, 49, 1, pp. 27-32, (2014)
  • [9] Zhai B., Jin W., Qin N., Running state estimation of high-speed train based on EEMD and hyper-sphere support vector machines, Computer Measurement and Control, 22, 8, pp. 2356-2369, (2014)
  • [10] Song Y., Characteristic analysis of vibration signals of high speed train based on CEEMD and feature fusion, (2016)