Sparse Feature Extraction for Variable Speed Machinery Based on Sparse Decomposition Combined GST

被引：0
|
作者：
Yan B.-K. [1 ]
Zhou F.-X. [2 ]
Xu B. [2 ]
机构：
[1] Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, 430081, Hubei
[2] Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, Hubei
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2019年 / 39卷 / 06期
关键词：
Feature extraction; Generalized S transform; Orthogonal matching pursuit(OMP); Sparse decomposition;
D O I：
10.15918/j.tbit1001-0645.2019.06.009
中图分类号：
学科分类号：
摘要：
In order to extract fault impulse feature of variable speed machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined generalized S transform (GST) was proposed in this paper. Firstly, multi-resolution generalized S transform (MGST) was used to pursuit the optimal atom in each iteration, to get normalized time-frequency spectrums with different scales, and to find the maximum energy and corresponding time-frequency factors to build an optimal atom. Then, an orthogonal matching pursuit (OMP) was used to decompose the signal into several optimal atoms, and the efficiency of atoms pursuit was improved with MGST. Finally, the theoretical locations of impulses were calculated according to the location of first impulse in the sparse representation signal, and the fault was diagnosed through the comparison of theoretical and measured locations. The results of simulation and experiment validate the performances of the proposed method, being better than traditional GST method and OMP method in precision and decomposition speed. © 2019, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：603 / 608
页数：5
相关论文
共 13 条
  • [1] Jiao J.F., Zhao N., Wang G., A nonlinear quality-related fault detection approach based on modified kernel partial least squares, ISA Transactions, 66, pp. 275-283, (2017)
  • [2] Liu Z.W., Guo W., Hu J.H., A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and twin SVM, ISA Transactions, 66, pp. 249-261, (2017)
  • [3] Boashash B., Khan N.A., Jabeur T.B., Time-frequency features for pattern recognition using high-resolution TFDs: a tutorial review, Digital Signal Processing, 40, pp. 1-30, (2015)
  • [4] Roopa S., Narasimhan S.V., S-transform based on analytic discrete cosine transform for time-frequency analysis, Signal Processing, 105, pp. 207-215, (2014)
  • [5] Liu Z.W., He Z.J., Guo W., A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery, ISA Transactions, 61, pp. 211-220, (2016)
  • [6] Wang L., Liu Z.W., Miao Q., Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis, Mechanical Systems and Signal Processing, 103, pp. 60-75, (2018)
  • [7] Chen J.L., Li Z.P., Pan J., Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review, Mechanical Systems and Signal Processing, 70-71, pp. 1-35, (2016)
  • [8] Mallat S.G., Zhang Z.F., Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, 41, 12, pp. 3397-3415, (1993)
  • [9] Donoho D.L., Tsaig Y., Drori I., Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit, IEEE Transactions on Information Theory, 58, 2, pp. 1094-1121, (2012)
  • [10] Yan B., Zhou F., Initial fault identificationof bearing based on coherent cumulant stagewise orthogonal matching pursuit, Journal of Mechanical Engineering, 50, 13, pp. 88-96, (2014)
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