Application of Maximum Overlap Discrete Wavelet Packet Transform Marginal Spectrum Features in Gear Fault Diagnosis

被引：1
|
作者：
Chen B. [1 ,2 ]
Huang W. [1 ,2 ]
Li L. [1 ,2 ]
Xiao W. [1 ,2 ]
Chen F. [1 ,2 ]
Xiao N. [1 ,2 ]
机构：
[1] Hubei Key Laboratory of Construction and Hydropower Engineering, China Three Gorges University, Yichang, 443002, Hubei
[2] College of Mechanical and Power, China Three Gorges University, Yichang, 443002, Hubei
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2020年 / 54卷 / 02期
关键词：
Empirical mode decomposition; Gear fault diagnosis; Hilbert marginal spectrum; Particle swarm optimization; Support vector machine;
D O I：
10.7652/xjtuxb202002005
中图分类号：
学科分类号：
摘要：
A fault diagnosis method based on the maximum overlap discrete wavelet packet transform (MODWPT) marginal spectrum features and the particle swarm optimization-support vector machine (PSO-SVM) is proposed to deal with the problem of fault mode confusion caused by multi-component band overlap of gear fault vibration signals. In order to reduce the influence of harmonics and noise on the fault mode component separation, MODWPT is used to decompose the collected experimental signals into 5 layers and to obtain 32 components. The first 16 components are selected based on the principle of the band energy dominant distribution to construct a Hilbert marginal spectrum. Then, the marginal spectrum features are extracted and considered as an input to the SVM with PSO parameter optimization for fault type identification. Analysis results of simulation signals show that the MODWPT marginal spectrum is superior to the EMD method in terms of anti-mode mixing, anti-end effect, and frequency extraction accuracy. The proposed method is applied to fault diagnosis of six different gear fault types. The normalized feature of MODWPT marginal spectrum has obvious hierarchical phenomenon of fault types, and the accuracy of identifying gear faults reaches 98%. These results show that the method has strong fault diagnosis capability. © 2020, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
引用
收藏
页码：35 / 42
页数：7
相关论文
共 16 条
  • [1] Huang N.E., Shen Z., Long S.R., Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
  • [2] Zhang C., Zhao R., Deng L., Fault diagnosis method of rolling bearing based on EEMD singular value entropy, Journal of Vibration, Measurement & Diagnosis, 39, 2, pp. 353-358, (2019)
  • [3] Wang Z., Wu W., Ma W., Et al., LMD-MS-based method for extracting weak faults of rolling bearings, Journal of Vibration, Measurement & Diagnosis, 38, 5, pp. 1014-1020, (2018)
  • [4] Chen B., He Z., Chen X., Et al., A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis, Measurement Science & Technology, 22, 5, (2011)
  • [5] Dragomiretskiy K., Zosso D., Variational mode decomposition, IEEE Transactions on Signal Processing, 62, 3, pp. 531-544, (2014)
  • [6] Cicone A., Liu J., Zhou H., Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis, Applied and Computational Harmonic Analysis, 41, 2, pp. 384-411, (2016)
  • [7] Li Y., Zhang J., Li Y., Wear state recognition of rolling bearings based on VMD-HMM, Journal of Vibration and Shock, 37, 21, pp. 61-67, (2018)
  • [8] Chen B., Wang X., Zhao C., Et al., Fault feature extraction of rolling bearing based on ALIF and energy operator demodulation, Journal of Nanjing University of Science and Technology, 42, 4, pp. 445-452, (2018)
  • [9] Jiang X., Wang J., Shi J., Et al., A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines, Mechanical Systems and Signal Processing, 116, pp. 668-692, (2019)
  • [10] Yang Y., He Y., Cheng J., Et al., Time-frequency analysis of Hilbert spectrum based on maximal overlap discrete wavelet packet transform, Journal of Vibration, Measurement & Diagnosis, 29, 1, pp. 10-13, (2009)
12